data
dict |
|---|
{
"proceeding": {
"id": "1H1gVMlkl32",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1H0KN0OXgGc",
"doi": "10.1109/CVPR52688.2022.00654",
"title": "Multimodal Colored Point Cloud to Image Alignment",
"normalizedTitle": "Multimodal Colored Point Cloud to Image Alignment",
"abstract": "Reconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RGB-D images. A major challenge in acquiring such ground truth data is the accurate alignment between RGB images and the point cloud measured by a depth scanner. To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching. In the proposed framework, the optimization minimizes the photometric difference between the colors of the point cloud and the corresponding colors of the image pixels. Unlike other methods that try to reduce this photometric error, we analyze the computation of the gradient on the image plane and propose a different direct scheme. We assume that the colors produced by the geometric scanner camera and the color camera sensor are different and therefore characterized by different chromatic acquisition properties. Under these multimodal conditions, we find the transformation between the camera image and the point cloud colors. We alternately optimize for aligning the position of the point cloud and matching the different color spaces. The alignments produced by the proposed method are demonstrated on both synthetic data with quantitative evaluation and real scenes with qualitative results.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Reconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RGB-D images. A major challenge in acquiring such ground truth data is the accurate alignment between RGB images and the point cloud measured by a depth scanner. To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching. In the proposed framework, the optimization minimizes the photometric difference between the colors of the point cloud and the corresponding colors of the image pixels. Unlike other methods that try to reduce this photometric error, we analyze the computation of the gradient on the image plane and propose a different direct scheme. We assume that the colors produced by the geometric scanner camera and the color camera sensor are different and therefore characterized by different chromatic acquisition properties. Under these multimodal conditions, we find the transformation between the camera image and the point cloud colors. We alternately optimize for aligning the position of the point cloud and matching the different color spaces. The alignments produced by the proposed method are demonstrated on both synthetic data with quantitative evaluation and real scenes with qualitative results.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Reconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RGB-D images. A major challenge in acquiring such ground truth data is the accurate alignment between RGB images and the point cloud measured by a depth scanner. To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching. In the proposed framework, the optimization minimizes the photometric difference between the colors of the point cloud and the corresponding colors of the image pixels. Unlike other methods that try to reduce this photometric error, we analyze the computation of the gradient on the image plane and propose a different direct scheme. We assume that the colors produced by the geometric scanner camera and the color camera sensor are different and therefore characterized by different chromatic acquisition properties. Under these multimodal conditions, we find the transformation between the camera image and the point cloud colors. We alternately optimize for aligning the position of the point cloud and matching the different color spaces. The alignments produced by the proposed method are demonstrated on both synthetic data with quantitative evaluation and real scenes with qualitative results.",
"fno": "694600g646",
"keywords": [
"Cameras",
"Image Colour Analysis",
"Image Reconstruction",
"Image Sensors",
"Learning Artificial Intelligence",
"Optical Scanners",
"RGB Images",
"Differential Optimization Method",
"Given Color Image",
"Iterative Geometric Color Matching",
"Photometric Difference",
"Corresponding Colors",
"Image Pixels",
"Image Plane",
"Different Direct Scheme",
"Geometric Scanner Camera",
"Color Camera Sensor",
"Different Chromatic Acquisition Properties",
"Camera Image",
"Point Cloud Colors",
"Different Color Spaces",
"Synthetic Data",
"Multimodal Colored Point Cloud",
"Image Alignment",
"Geometric Structures",
"Supervised Learning Suffers",
"Real World RGB D",
"Ground Truth Data",
"Point Cloud Compression",
"Solid Modeling",
"Three Dimensional Displays",
"Image Color Analysis",
"Supervised Learning",
"Pose Estimation",
"Pipelines"
],
"authors": [
{
"affiliation": "Technion - Israel Institute of Technology",
"fullName": "Noam Rotstein",
"givenName": "Noam",
"surname": "Rotstein",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Technion - Israel Institute of Technology",
"fullName": "Amit Bracha",
"givenName": "Amit",
"surname": "Bracha",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Technion - Israel Institute of Technology",
"fullName": "Ron Kimmel",
"givenName": "Ron",
"surname": "Kimmel",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-06-01T00:00:00",
"pubType": "proceedings",
"pages": "6646-6656",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6946-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1H0KMXk5JiU",
"name": "pcvpr202269460-09879894s1-mm_694600g646.zip",
"size": "7.25 MB",
"location": "https://www.computer.org/csdl/api/v1/extra/pcvpr202269460-09879894s1-mm_694600g646.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "694600g636",
"articleId": "1H1mqlBmANi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "694600g657",
"articleId": "1H0NiTg027C",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/isdea/2012/4608/0/4608a648",
"title": "Online Colored Point Cloud Acquisition by Reprojection",
"doi": null,
"abstractUrl": "/proceedings-article/isdea/2012/4608a648/12OmNAle6NH",
"parentPublication": {
"id": "proceedings/isdea/2012/4608/0",
"title": "2012 Second International Conference on Intelligent System Design and Engineering Application",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032a143",
"title": "Colored Point Cloud Registration Revisited",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032a143/12OmNzXWZKe",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2008/02/ttg2008020440",
"title": "A Generic Scheme for Progressive Point Cloud Coding",
"doi": null,
"abstractUrl": "/journal/tg/2008/02/ttg2008020440/13rRUNvgz49",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200d293",
"title": "PICCOLO: Point Cloud-Centric Omnidirectional Localization",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200d293/1BmFx90D0n6",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200p5490",
"title": "ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5490/1BmLgVFEs3C",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09756929",
"title": "Perceptual Quality Assessment of Colored 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09756929/1Cxva6pb2iA",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600t9718",
"title": "Point Cloud Color Constancy",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600t9718/1H0NbRwSzHq",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2022/5670/0/567000a104",
"title": "Efficient Human Pose Estimation via 3D Event Point Cloud",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2022/567000a104/1KYswqRo9eE",
"parentPublication": {
"id": "proceedings/3dv/2022/5670/0",
"title": "2022 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600b196",
"title": "Centroid Distance Keypoint Detector for Colored Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600b196/1LiO8nAtFok",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icis-fall/2021/7679/0/09627447",
"title": "Point Cloud-based 3D Underwater Pose Estimation Using RANSAC and VFH Descriptors",
"doi": null,
"abstractUrl": "/proceedings-article/icis-fall/2021/09627447/1z7dKCFGdyg",
"parentPublication": {
"id": "proceedings/icis-fall/2021/7679/0",
"title": "2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1H1gVMlkl32",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1H0LihR7AqI",
"doi": "10.1109/CVPR52688.2022.02050",
"title": "No-Reference Point Cloud Quality Assessment via Domain Adaptation",
"normalizedTitle": "No-Reference Point Cloud Quality Assessment via Domain Adaptation",
"abstract": "We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the evaluation criteria of human perception via DNN and transfer the capability of prediction to 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. To extract effective latent features and minimize the domain discrepancy, we propose a hierarchical feature encoder and a conditional-discriminative network. Considering that the ultimate pur-pose is regressing objective score, we introduce a novel con-ditional cross entropy loss in the conditional-discriminative network to penalize the negative samples which hinder the convergence of the quality regression network. Experi-mental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics. The proposed method also suggests the feasibility of assessing the quality of specific media content without the expensive and cumbersome subjective evaluations. Code is available at https://github.com/Qi-Yangsjtu/IT-PCQA.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the evaluation criteria of human perception via DNN and transfer the capability of prediction to 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. To extract effective latent features and minimize the domain discrepancy, we propose a hierarchical feature encoder and a conditional-discriminative network. Considering that the ultimate pur-pose is regressing objective score, we introduce a novel con-ditional cross entropy loss in the conditional-discriminative network to penalize the negative samples which hinder the convergence of the quality regression network. Experi-mental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics. The proposed method also suggests the feasibility of assessing the quality of specific media content without the expensive and cumbersome subjective evaluations. Code is available at https://github.com/Qi-Yangsjtu/IT-PCQA.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the evaluation criteria of human perception via DNN and transfer the capability of prediction to 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. To extract effective latent features and minimize the domain discrepancy, we propose a hierarchical feature encoder and a conditional-discriminative network. Considering that the ultimate pur-pose is regressing objective score, we introduce a novel con-ditional cross entropy loss in the conditional-discriminative network to penalize the negative samples which hinder the convergence of the quality regression network. Experi-mental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics. The proposed method also suggests the feasibility of assessing the quality of specific media content without the expensive and cumbersome subjective evaluations. Code is available at https://github.com/Qi-Yangsjtu/IT-PCQA.",
"fno": "694600v1147",
"keywords": [
"Entropy",
"Feature Extraction",
"Learning Artificial Intelligence",
"Neural Nets",
"Regression Analysis",
"Target Domain",
"Infer Point Cloud Quality",
"Unsupervised Adversarial Domain Adaptation",
"Domain Discrepancy",
"Conditional Discriminative Network",
"Quality Regression Network",
"No Reference Metrics",
"Full Reference Metrics",
"Reference Point Cloud Quality Assessment",
"No Reference Quality Assessment Metric",
"Deep Neural Network",
"DNN",
"No Reference Metric Design",
"No Reference PCQA",
"Large Scale Subjective Databases",
"Robust Networks",
"Rich Subjective Scores",
"Natural Images",
"Source Domain",
"Point Cloud Compression",
"Measurement",
"Solid Modeling",
"Three Dimensional Displays",
"Databases",
"Transfer Learning",
"Media"
],
"authors": [
{
"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center",
"fullName": "Qi Yang",
"givenName": "Qi",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center",
"fullName": "Yipeng Liu",
"givenName": "Yipeng",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center",
"fullName": "Siheng Chen",
"givenName": "Siheng",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center",
"fullName": "Yiling Xu",
"givenName": "Yiling",
"surname": "Xu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghai Jiao Tong University,Cooperative Medianet Innovation Center",
"fullName": "Jun Sun",
"givenName": "Jun",
"surname": "Sun",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-06-01T00:00:00",
"pubType": "proceedings",
"pages": "21147-21156",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6946-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "694600v1136",
"articleId": "1H1hALxNvTa",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "694600v1157",
"articleId": "1H0N1fCX36o",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bracis/2016/3566/0/07839574",
"title": "No-Reference Image Quality Assessment Using Texture Information Banks",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2016/07839574/12OmNAiFIc4",
"parentPublication": {
"id": "proceedings/bracis/2016/3566/0",
"title": "2016 5th Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ewdts/2016/0693/0/07807685",
"title": "No-reference image quality assessment based on local binary patterns",
"doi": null,
"abstractUrl": "/proceedings-article/ewdts/2016/07807685/12OmNqAU6AY",
"parentPublication": {
"id": "proceedings/ewdts/2016/0693/0",
"title": "2016 IEEE East-West Design & Test Symposium (EWDTS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/is3c/2016/3071/0/3071b093",
"title": "No-Reference Image Quality Assessment Based on HVS",
"doi": null,
"abstractUrl": "/proceedings-article/is3c/2016/3071b093/12OmNwEJ0Ir",
"parentPublication": {
"id": "proceedings/is3c/2016/3071/0",
"title": "2016 International Symposium on Computer, Consumer and Control (IS3C)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/greencom-ithingscpscom/2013/5046/0/06682341",
"title": "Saliency-Based Feature Learning for No-Reference Image Quality Assessment",
"doi": null,
"abstractUrl": "/proceedings-article/greencom-ithingscpscom/2013/06682341/12OmNwKoZdH",
"parentPublication": {
"id": "proceedings/greencom-ithingscpscom/2013/5046/0",
"title": "2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmip/2016/8940/0/8940a051",
"title": "No-Reference Image Quality Assessment for Defocus Restoration",
"doi": null,
"abstractUrl": "/proceedings-article/icmip/2016/8940a051/12OmNwO5LVT",
"parentPublication": {
"id": "proceedings/icmip/2016/8940/0",
"title": "2016 First International Conference on Multimedia and Image Processing (ICMIP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2017/3013/0/3013a224",
"title": "CNN-MR for No Reference Video Quality Assessment",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2017/3013a224/12OmNykTNn5",
"parentPublication": {
"id": "proceedings/icisce/2017/3013/0",
"title": "2017 4th International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscid/2008/3311/2/3311b161",
"title": "Reference Point Determination in Enhanced Fingerprint Image",
"doi": null,
"abstractUrl": "/proceedings-article/iscid/2008/3311b161/12OmNyo1nMC",
"parentPublication": {
"id": "proceedings/iscid/2008/3311/2",
"title": "2008 International Symposium on Computational Intelligence and Design",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmtma/2022/9978/0/997800a397",
"title": "Progress of No-Reference Image Quality Assessment Based on Deep Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2022/997800a397/1ByeShFkNos",
"parentPublication": {
"id": "proceedings/icmtma/2022/9978/0",
"title": "2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09756929",
"title": "Perceptual Quality Assessment of Colored 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09756929/1Cxva6pb2iA",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sitis/2022/6495/0/649500a616",
"title": "3D point cloud quality assessment method using Mahalanobis distance",
"doi": null,
"abstractUrl": "/proceedings-article/sitis/2022/649500a616/1MeoL090t9e",
"parentPublication": {
"id": "proceedings/sitis/2022/6495/0",
"title": "2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1H1gVMlkl32",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1H0LpmYzOeI",
"doi": "10.1109/CVPR52688.2022.01440",
"title": "3DAC: Learning Attribute Compression for Point Clouds",
"normalizedTitle": "3DAC: Learning Attribute Compression for Point Clouds",
"abstract": "We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed method.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed method.",
"fno": "694600o4799",
"keywords": [
"Data Compression",
"Entropy",
"Image Reconstruction",
"Learning Artificial Intelligence",
"Probability",
"Solid Modelling",
"Transforms",
"Previous Encoded Attributes",
"Compress",
"Transform Coefficients",
"Final Attributes Bitstream",
"Large Scale Open Point Cloud Datasets",
"Superior Compression Rates",
"Attribute Compression",
"Point Clouds",
"Large Scale Unstructured 3 D",
"Different Encoding Steps",
"Different Attribute Channels",
"Deep Compression Network",
"Termed 3 DAC",
"Point Cloud Attributes",
"Deep Entropy Model",
"Attribute Transforms",
"Point Cloud Compression",
"Reflectivity",
"Computer Vision",
"Three Dimensional Displays",
"Correlation",
"Transforms",
"Entropy"
],
"authors": [
{
"affiliation": "Sun Yat-sen University",
"fullName": "Guangchi Fang",
"givenName": "Guangchi",
"surname": "Fang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Oxford",
"fullName": "Qingyong Hu",
"givenName": "Qingyong",
"surname": "Hu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Information Engineering University",
"fullName": "Hanyun Wang",
"givenName": "Hanyun",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghai Jiaotong University",
"fullName": "Yiling Xu",
"givenName": "Yiling",
"surname": "Xu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Sun Yat-sen University",
"fullName": "Yulan Guo",
"givenName": "Yulan",
"surname": "Guo",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-06-01T00:00:00",
"pubType": "proceedings",
"pages": "14799-14808",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6946-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1H0LpjLjoVq",
"name": "pcvpr202269460-09879523s1-mm_694600o4799.zip",
"size": "3.26 MB",
"location": "https://www.computer.org/csdl/api/v1/extra/pcvpr202269460-09879523s1-mm_694600o4799.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "694600o4789",
"articleId": "1H1lYU62Xu0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "694600o4809",
"articleId": "1H1llfiVtXa",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/dcc/2016/1853/0/07786158",
"title": "Point Cloud Attribute Compression Using 3-D Intra Prediction and Shape-Adaptive Transforms",
"doi": null,
"abstractUrl": "/proceedings-article/dcc/2016/07786158/12OmNBUS7ce",
"parentPublication": {
"id": "proceedings/dcc/2016/1853/0",
"title": "2016 Data Compression Conference (DCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2017/6067/0/08019426",
"title": "Hybrid color attribute compression for point cloud data",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2017/08019426/12OmNxQOjF2",
"parentPublication": {
"id": "proceedings/icme/2017/6067/0",
"title": "2017 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/2006/2686/0/26860229",
"title": "Point set compression through BSP quantization",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2006/26860229/12OmNzFdt7I",
"parentPublication": {
"id": "proceedings/sibgrapi/2006/2686/0",
"title": "2006 19th Brazilian Symposium on Computer Graphics and Image Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/12/ttg2012122295",
"title": "An Adaptive Prediction-Based Approach to Lossless Compression of Floating-Point Volume Data",
"doi": null,
"abstractUrl": "/journal/tg/2012/12/ttg2012122295/13rRUx0xPID",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/02/09736689",
"title": "PointGLR: Unsupervised Structural Representation Learning of 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/2023/02/09736689/1BN1Ot4gcjm",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09858927",
"title": "A Novel Grid-Based Geometry Compression Framework for Spinning Lidar Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09858927/1G9EN6WL3KE",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859998",
"title": "Fine-Grained Correlation Representation for Graph-Based Point Cloud Attribute Compression",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859998/1G9EaP0htRu",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600c323",
"title": "Density-preserving Deep Point Cloud Compression",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600c323/1H0ODd9D5uM",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/micro/2022/6272/0/627200a282",
"title": "Pushing Point Cloud Compression to the Edge",
"doi": null,
"abstractUrl": "/proceedings-article/micro/2022/627200a282/1HMSGnIWJrO",
"parentPublication": {
"id": "proceedings/micro/2022/6272/0",
"title": "2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/09968173",
"title": "Sparse Tensor-Based Multiscale Representation for Point Cloud Geometry Compression",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/09968173/1IKD7VXXRhm",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KxUhhFgzlK",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"acronym": "wacv",
"groupId": "1000040",
"volume": "0",
"displayVolume": "0",
"year": "2023",
"__typename": "ProceedingType"
},
"article": {
"id": "1LiO8nAtFok",
"doi": "10.1109/WACV56688.2023.00125",
"title": "Centroid Distance Keypoint Detector for Colored Point Clouds",
"normalizedTitle": "Centroid Distance Keypoint Detector for Colored Point Clouds",
"abstract": "Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) key- point detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art key- point detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of- the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) key- point detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art key- point detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of- the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) key- point detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art key- point detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of- the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector.",
"fno": "934600b196",
"keywords": [
"C Language",
"Eigenvalues And Eigenfunctions",
"Feature Extraction",
"Geometry",
"Image Colour Analysis",
"Image Registration",
"Object Detection",
"Robot Vision",
"C Implementation",
"CED Keypoint Detector",
"Centroid Distance Keypoint Detector",
"Color Information",
"Color Space",
"Color Salient Keypoints",
"Colored Point Cloud Registration",
"Computer Vision",
"Eigenvalue Decomposition",
"Geometry Salient Keypoints",
"Learning Based Keypoint Detectors",
"Multimodal Keypoint Detector",
"Robotics Applications",
"Point Cloud Compression",
"Computer Vision",
"Three Dimensional Displays",
"Navigation",
"Estimation",
"Detectors",
"Color",
"Algorithms 3 D Computer Vision",
"Robotics"
],
"authors": [
{
"affiliation": "University of California,Riverside,United States of America",
"fullName": "Hanzhe Teng",
"givenName": "Hanzhe",
"surname": "Teng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of California,Riverside,United States of America",
"fullName": "Dimitrios Chatziparaschis",
"givenName": "Dimitrios",
"surname": "Chatziparaschis",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of California,Riverside,United States of America",
"fullName": "Xinyue Kan",
"givenName": "Xinyue",
"surname": "Kan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of California,Riverside,United States of America",
"fullName": "Amit K. Roy-Chowdhury",
"givenName": "Amit K.",
"surname": "Roy-Chowdhury",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of California,Riverside,United States of America",
"fullName": "Konstantinos Karydis",
"givenName": "Konstantinos",
"surname": "Karydis",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wacv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2023-01-01T00:00:00",
"pubType": "proceedings",
"pages": "1196-1205",
"year": "2023",
"issn": null,
"isbn": "978-1-6654-9346-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "934600b186",
"articleId": "1LiO79JjXdm",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "934600b206",
"articleId": "1L8qrk7ZfNu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2017/0457/0/0457e645",
"title": "Hand Keypoint Detection in Single Images Using Multiview Bootstrapping",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457e645/12OmNBpEeXR",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2015/8391/0/8391c318",
"title": "Learning a Descriptor-Specific 3D Keypoint Detector",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2015/8391c318/12OmNs0TL2X",
"parentPublication": {
"id": "proceedings/iccv/2015/8391/0",
"title": "2015 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032d047",
"title": "A Coarse-Fine Network for Keypoint Localization",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032d047/12OmNzmLxND",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600b716",
"title": "LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600b716/1H0Kwo5tABi",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600b172",
"title": "OSKDet: Orientation-sensitive Keypoint Localization for Rotated Object Detection",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600b172/1H1hQvRYcbm",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600r7021",
"title": "UKPGAN: A General Self-Supervised Keypoint Detector",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600r7021/1H1iBLwdhbq",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956255",
"title": "Integrated Deconvolution Keypoint Detector and Descriptor Network",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956255/1IHqdUPXtNm",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300a753",
"title": "MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300a753/1hVlOob3kxq",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ism/2020/8697/0/869700a127",
"title": "Automatic Sparsity-Aware Recognition for Keypoint Detection",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2020/869700a127/1qBbII1cKQ0",
"parentPublication": {
"id": "proceedings/ism/2020/8697/0",
"title": "2020 IEEE International Symposium on Multimedia (ISM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900a043",
"title": "Skeleton Merger: an Unsupervised Aligned Keypoint Detector",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900a043/1yeKLLprk4w",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNy314bx",
"title": "2017 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"acronym": "wacv",
"groupId": "1000040",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNAmVH7L",
"doi": "10.1109/WACV.2017.131",
"title": "Deep Feature Consistent Variational Autoencoder",
"normalizedTitle": "Deep Feature Consistent Variational Autoencoder",
"abstract": "We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.",
"fno": "07926714",
"keywords": [
"Feedforward Neural Nets",
"Image Reconstruction",
"Deep Feature Consistency",
"Variational Autoencoder",
"Spatial Correlation Characteristics",
"Natural Visual Appearance",
"Perceptual Quality",
"Pretrained Deep CNN",
"Convolutional Neural Network",
"Feature Perceptual Loss",
"VAE Training",
"Celeb A Face Dataset",
"Latent Vectors",
"Facial Attribute Prediction",
"Image Reconstruction",
"Loss Measurement",
"Image Reconstruction",
"Training",
"Decoding",
"Face",
"Correlation",
"Feature Extraction"
],
"authors": [
{
"affiliation": null,
"fullName": "Xianxu Hou",
"givenName": "Xianxu",
"surname": "Hou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Linlin Shen",
"givenName": "Linlin",
"surname": "Shen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Ke Sun",
"givenName": "Ke",
"surname": "Sun",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Guoping Qiu",
"givenName": "Guoping",
"surname": "Qiu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wacv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-03-01T00:00:00",
"pubType": "proceedings",
"pages": "1133-1141",
"year": "2017",
"issn": null,
"isbn": "978-1-5090-4822-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "07926713",
"articleId": "12OmNBKW9AZ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07926715",
"articleId": "12OmNxisQUF",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2017/1032/0/1032d209",
"title": "DeepCoder: Semi-Parametric Variational Autoencoders for Automatic Facial Action Coding",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032d209/12OmNC8MsLq",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2018/4886/0/488601b312",
"title": "Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2018/488601b312/12OmNzahccZ",
"parentPublication": {
"id": "proceedings/wacv/2018/4886/0",
"title": "2018 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956102",
"title": "Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956102/1IHoKnmrDlC",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2019/9552/0/955200b354",
"title": "Data Augmentation for Monaural Singing Voice Separation Based on Variational Autoencoder-Generative Adversarial Network",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2019/955200b354/1cdOQFsZ1WU",
"parentPublication": {
"id": "proceedings/icme/2019/9552/0",
"title": "2019 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2019/6092/0/609200a151",
"title": "Radio Galaxy Morphology Simulation via Residual Conditional Variational Autoencoder",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2019/609200a151/1i5m1RVZMsM",
"parentPublication": {
"id": "proceedings/cis/2019/6092/0",
"title": "2019 15th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150876",
"title": "OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150876/1lPHq6GtXO0",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150958",
"title": "Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150958/1lPHuX0nbc4",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h917",
"title": "Guided Variational Autoencoder for Disentanglement Learning",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h917/1m3oiUnuaIM",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccst/2020/8138/0/813800a394",
"title": "An intelligent music generation based on Variational Autoencoder",
"doi": null,
"abstractUrl": "/proceedings-article/iccst/2020/813800a394/1p1gpeuOxb2",
"parentPublication": {
"id": "proceedings/iccst/2020/8138/0",
"title": "2020 International Conference on Culture-oriented Science & Technology (ICCST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ai/2022/02/09536242",
"title": "Fault Diagnosis of Machines Using Deep Convolutional Beta-Variational Autoencoder",
"doi": null,
"abstractUrl": "/journal/ai/2022/02/09536242/1wREij5jnby",
"parentPublication": {
"id": "trans/ai",
"title": "IEEE Transactions on Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNAXxXaK",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"acronym": "iccv",
"groupId": "1000149",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNCd2rI2",
"doi": "10.1109/ICCV.2017.117",
"title": "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression",
"normalizedTitle": "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression",
"abstract": "3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Code and models will be made available at http://aaronsplace.co.uk.",
"abstracts": [
{
"abstractType": "Regular",
"content": "3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Code and models will be made available at http://aaronsplace.co.uk.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Code and models will be made available at http://aaronsplace.co.uk.",
"fno": "1032b031",
"keywords": [
"Computer Vision",
"Face Recognition",
"Image Reconstruction",
"Neural Nets",
"Pose Estimation",
"Regression Analysis",
"Facial Expressions",
"Reconstruction Quality",
"Facial Landmark Localization",
"Single 2 D Image",
"Volumetric Representation",
"Direct Regression",
"Simple CNN Architecture",
"3 D Morphable Model",
"Nonvisible Parts",
"Whole 3 D Facial Geometry",
"Arbitrary Facial Poses",
"Single 2 D Facial Image",
"CNN Works",
"Convolutional Neural Network",
"General These Methods",
"Nonuniform Illumination",
"Dense Correspondence",
"Methodological Challenges",
"Multiple Facial Images",
"Fundamental Computer Vision Problem",
"Direct Volumetric CNN Regression",
"Single Image",
"Pose 3 D",
"Three Dimensional Displays",
"Face",
"Two Dimensional Displays",
"Image Reconstruction",
"Geometry",
"Shape",
"Optimization"
],
"authors": [
{
"affiliation": null,
"fullName": "Aaron S. Jackson",
"givenName": "Aaron S.",
"surname": "Jackson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Adrian Bulat",
"givenName": "Adrian",
"surname": "Bulat",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Vasileios Argyriou",
"givenName": "Vasileios",
"surname": "Argyriou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Georgios Tzimiropoulos",
"givenName": "Georgios",
"surname": "Tzimiropoulos",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-10-01T00:00:00",
"pubType": "proceedings",
"pages": "1031-1039",
"year": "2017",
"issn": "2380-7504",
"isbn": "978-1-5386-1032-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "1032b021",
"articleId": "12OmNzX6ctJ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "1032b040",
"articleId": "12OmNAkEU63",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/fg/2018/2335/0/233501a210",
"title": "Face Alignment across Large Pose via MT-CNN Based 3D Shape Reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2018/233501a210/12OmNAlvHQI",
"parentPublication": {
"id": "proceedings/fg/2018/2335/0",
"title": "2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032d219",
"title": "Pose-Invariant Face Alignment with a Single CNN",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032d219/12OmNCcbEhK",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2017/0563/0/08273654",
"title": "CNN based 3D facial expression recognition using masking and landmark features",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2017/08273654/12OmNrAMEJB",
"parentPublication": {
"id": "proceedings/acii/2017/0563/0",
"title": "2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2017/1034/0/1034c174",
"title": "3D Pose Regression Using Convolutional Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2017/1034c174/12OmNxEBzn3",
"parentPublication": {
"id": "proceedings/iccvw/2017/1034/0",
"title": "2017 IEEE International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2017/0457/0/0457f553",
"title": "Learning Detailed Face Reconstruction from a Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457f553/12OmNxvO05B",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/5209a465",
"title": "Real-Time Pose-Invariant Face Recognition by Triplet Pose Sparse Matrix from Only a Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209a465/12OmNz61d2Y",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2016/5407/0/5407a441",
"title": "Robust Real-Time 3D Face Tracking from RGBD Videos under Extreme Pose, Depth, and Expression Variation",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2016/5407a441/12OmNzC5TfM",
"parentPublication": {
"id": "proceedings/3dv/2016/5407/0",
"title": "2016 Fourth International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000d897",
"title": "RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000d897/17D45VVho4R",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300j428",
"title": "Photo-Realistic Facial Details Synthesis From Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300j428/1hVlh0SqiPe",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2020/1331/0/09102811",
"title": "Expression-Aware Face Reconstruction Via A Dual-Stream Network",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2020/09102811/1kwr15w4dQQ",
"parentPublication": {
"id": "proceedings/icme/2020/1331/0",
"title": "2020 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNBTawn8",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"acronym": "cvprw",
"groupId": "1001809",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNCmpcLR",
"doi": "10.1109/CVPRW.2014.7",
"title": "Improving 3D Face Details Based on Normal Map of Hetero-source Images",
"normalizedTitle": "Improving 3D Face Details Based on Normal Map of Hetero-source Images",
"abstract": "For each person, there exist large unstructured photo collections in personal photo albums. We call these photos Hetero-source images, which imply abundant shape and texture information of the specific face. In this paper, we propose a novel 3D face modeling method combining the normal map of Hetero-source images with the fitting result based on a single image to achieve more accurate 3D shape estimates. Based on recent research showing that the set of images of convex Lambertian surfaces under general illumination can be well approximated using low-order spherical harmonics, we first incorporate spherical harmonics into the 3D morphable model to initialize the 3D shape. The fitting result, however, suffers from model dominance and lacks of fine details. The normal map inferred by Hetero-source image shading constraints allows the possibility of improving local details and challenging the model dominance. We estimate the normal map which contains more accurate orientation information in an alternating optimization way and apply it to improve the preliminary 3D surface. Experimental results on both synthetic and real world data demonstrate that our method could be used to capture discriminating facial features and outperforms the single image fitting result in accuracy.",
"abstracts": [
{
"abstractType": "Regular",
"content": "For each person, there exist large unstructured photo collections in personal photo albums. We call these photos Hetero-source images, which imply abundant shape and texture information of the specific face. In this paper, we propose a novel 3D face modeling method combining the normal map of Hetero-source images with the fitting result based on a single image to achieve more accurate 3D shape estimates. Based on recent research showing that the set of images of convex Lambertian surfaces under general illumination can be well approximated using low-order spherical harmonics, we first incorporate spherical harmonics into the 3D morphable model to initialize the 3D shape. The fitting result, however, suffers from model dominance and lacks of fine details. The normal map inferred by Hetero-source image shading constraints allows the possibility of improving local details and challenging the model dominance. We estimate the normal map which contains more accurate orientation information in an alternating optimization way and apply it to improve the preliminary 3D surface. Experimental results on both synthetic and real world data demonstrate that our method could be used to capture discriminating facial features and outperforms the single image fitting result in accuracy.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "For each person, there exist large unstructured photo collections in personal photo albums. We call these photos Hetero-source images, which imply abundant shape and texture information of the specific face. In this paper, we propose a novel 3D face modeling method combining the normal map of Hetero-source images with the fitting result based on a single image to achieve more accurate 3D shape estimates. Based on recent research showing that the set of images of convex Lambertian surfaces under general illumination can be well approximated using low-order spherical harmonics, we first incorporate spherical harmonics into the 3D morphable model to initialize the 3D shape. The fitting result, however, suffers from model dominance and lacks of fine details. The normal map inferred by Hetero-source image shading constraints allows the possibility of improving local details and challenging the model dominance. We estimate the normal map which contains more accurate orientation information in an alternating optimization way and apply it to improve the preliminary 3D surface. Experimental results on both synthetic and real world data demonstrate that our method could be used to capture discriminating facial features and outperforms the single image fitting result in accuracy.",
"fno": "4308a009",
"keywords": [
"Shape",
"Three Dimensional Displays",
"Lighting",
"Face",
"Solid Modeling",
"Harmonic Analysis",
"Image Reconstruction"
],
"authors": [
{
"affiliation": null,
"fullName": "Chang Yang",
"givenName": "Chang",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jiansheng Chen",
"givenName": "Jiansheng",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Nan Su",
"givenName": "Nan",
"surname": "Su",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Guangda Su",
"givenName": "Guangda",
"surname": "Su",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvprw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-06-01T00:00:00",
"pubType": "proceedings",
"pages": "9-14",
"year": "2014",
"issn": "2160-7516",
"isbn": "978-1-4799-4308-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4308a001",
"articleId": "12OmNAm4TJe",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4308a015",
"articleId": "12OmNARRYAc",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2017/1032/0/1032c344",
"title": "3D Surface Detail Enhancement from a Single Normal Map",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032c344/12OmNwCsdKG",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2017/0457/0/0457f464",
"title": "3D Face Morphable Models \"In-the-Wild\"",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457f464/12OmNxXUhUV",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/06977406",
"title": "Robust 3D Morphable Model Fitting by Sparse SIFT Flow",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/06977406/12OmNyv7maW",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032d885",
"title": "Efficient Global Illumination for Morphable Models",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032d885/12OmNzt0IHc",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2016/8851/0/8851f479",
"title": "Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2016/8851f479/12OmNzvQI3W",
"parentPublication": {
"id": "proceedings/cvpr/2016/8851/0",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2017/11/07776921",
"title": "Adaptive 3D Face Reconstruction from Unconstrained Photo Collections",
"doi": null,
"abstractUrl": "/journal/tp/2017/11/07776921/13rRUxAAT8W",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2005/05/i0684",
"title": "Acquiring Linear Subspaces for Face Recognition under Variable Lighting",
"doi": null,
"abstractUrl": "/journal/tp/2005/05/i0684/13rRUxcsYN1",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2019/1975/0/197500c146",
"title": "Illumination-Invariant Face Recognition With Deep Relit Face Images",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2019/197500c146/18j8HRRf584",
"parentPublication": {
"id": "proceedings/wacv/2019/1975/0",
"title": "2019 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09756299",
"title": "Semantically Disentangled Variational Autoencoder for Modeling 3D Facial Details",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09756299/1CvQiJgja2k",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800a737",
"title": "Lightweight Photometric Stereo for Facial Details Recovery",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800a737/1m3nsjiR8Ck",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1BmEezmpGrm",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"acronym": "iccv",
"groupId": "1000149",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1BmGhEwLQL6",
"doi": "10.1109/ICCV48922.2021.01363",
"title": "VariTex: Variational Neural Face Textures",
"normalizedTitle": "VariTex: Variational Neural Face Textures",
"abstract": "Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate complete images of human heads, we propose an additive decoder that adds plausible details such as hair. A novel training scheme enforces a pose-independent latent space and in consequence, allows learning a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities under fine-grained control over head pose, face shape, and facial expressions. This facilitates a broad range of downstream tasks, like sampling novel identities, changing the head pose, expression transfer, and more.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate complete images of human heads, we propose an additive decoder that adds plausible details such as hair. A novel training scheme enforces a pose-independent latent space and in consequence, allows learning a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities under fine-grained control over head pose, face shape, and facial expressions. This facilitates a broad range of downstream tasks, like sampling novel identities, changing the head pose, expression transfer, and more.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate complete images of human heads, we propose an additive decoder that adds plausible details such as hair. A novel training scheme enforces a pose-independent latent space and in consequence, allows learning a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities under fine-grained control over head pose, face shape, and facial expressions. This facilitates a broad range of downstream tasks, like sampling novel identities, changing the head pose, expression transfer, and more.",
"fno": "281200n3870",
"keywords": [
"Hair",
"Training",
"Geometry",
"Codes",
"Shape",
"Ear",
"Aerospace Electronics",
"Image And Video Synthesis",
"Faces",
"Neural Generative Models"
],
"authors": [
{
"affiliation": "ETH Zurich",
"fullName": "Marcel C. Bühler",
"givenName": "Marcel C.",
"surname": "Bühler",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Google",
"fullName": "Abhimitra Meka",
"givenName": "Abhimitra",
"surname": "Meka",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "ETH Zurich",
"fullName": "Gengyan Li",
"givenName": "Gengyan",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Google",
"fullName": "Thabo Beeler",
"givenName": "Thabo",
"surname": "Beeler",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "ETH Zurich",
"fullName": "Otmar Hilliges",
"givenName": "Otmar",
"surname": "Hilliges",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-10-01T00:00:00",
"pubType": "proceedings",
"pages": "13870-13879",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-2812-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "281200n3859",
"articleId": "1BmGuxEHMIg",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "281200n3880",
"articleId": "1BmEMYFS8PC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200m2096",
"title": "Face Image Retrieval with Attribute Manipulation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200m2096/1BmLms5zisU",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2023/4544/0/10042744",
"title": "StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2023/10042744/1KOv2xt3tZK",
"parentPublication": {
"id": "proceedings/fg/2023/4544/0",
"title": "2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2019/3888/0/08925472",
"title": "Pose-Informed Face Alignment for Extreme Head Pose Variations in Animals",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2019/08925472/1fHGBBxU1NK",
"parentPublication": {
"id": "proceedings/acii/2019/3888/0",
"title": "2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800j240",
"title": "Interpreting the Latent Space of GANs for Semantic Face Editing",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800j240/1m3nRsHWYJa",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800f325",
"title": "FReeNet: Multi-Identity Face Reenactment",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800f325/1m3neHAcbKM",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h707",
"title": "Deep 3D Portrait From a Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h707/1m3nijIcYta",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/04/09241434",
"title": "InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs",
"doi": null,
"abstractUrl": "/journal/tp/2022/04/09241434/1ogEwfwfCjC",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412013",
"title": "Pose-robust Face Recognition by Deep Meta Capsule network-based Equivariant Embedding",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412013/1tmiOSwwzQs",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/4.509E179",
"title": "Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/4.509E179/1yeLFZU81zy",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/04/09668999",
"title": "Cross-Domain and Disentangled Face Manipulation With 3D Guidance",
"doi": null,
"abstractUrl": "/journal/tg/2023/04/09668999/1zTfZzq1wqY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KOuVybvP20",
"title": "2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)",
"acronym": "fg",
"groupId": "1000065",
"volume": "0",
"displayVolume": "0",
"year": "2023",
"__typename": "ProceedingType"
},
"article": {
"id": "1KOv1LhvlYI",
"doi": "10.1109/FG57933.2023.10042668",
"title": "DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features",
"normalizedTitle": "DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features",
"abstract": "Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing.",
"fno": "10042668",
"keywords": [
"Emotion Recognition",
"Face Recognition",
"Feature Extraction",
"Image Reconstruction",
"Disentangle Expression",
"Disentangled Variational Autoencoder",
"Dis VAE",
"Expression Components",
"Extracted Expression Features",
"Facial Expression Editing",
"Facial Expression Feature Extraction Suffers",
"Facial Expression Recognition",
"Facial Features",
"Facial Image Reconstruction",
"High Inter Subject Variations",
"High Quality Expression Features",
"High Quality Facial Expression Features",
"Identity Attributes",
"Identity Features",
"Identity Related Features",
"Identity Related Personal Attributes",
"Related Facial Expression Tasks",
"Remaining Features",
"Image Recognition",
"Face Recognition",
"Interference",
"Gesture Recognition",
"Feature Extraction",
"Decoding",
"Task Analysis"
],
"authors": [
{
"affiliation": "Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing,China",
"fullName": "Tianhao Wang",
"givenName": "Tianhao",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing,China",
"fullName": "Mingyue Zhang",
"givenName": "Mingyue",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing,China",
"fullName": "Lin Shang",
"givenName": "Lin",
"surname": "Shang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "fg",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2023-01-01T00:00:00",
"pubType": "proceedings",
"pages": "1-8",
"year": "2023",
"issn": null,
"isbn": "979-8-3503-4544-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "10042643",
"articleId": "1KOv2eD07Kg",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10042802",
"articleId": "1KOuY2JwWTS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/acii/2017/0563/0/08273626",
"title": "Photorealistic facial expression synthesis by the conditional difference adversarial autoencoder",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2017/08273626/12OmNvD8RE4",
"parentPublication": {
"id": "proceedings/acii/2017/0563/0",
"title": "2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/uic-atc-scalcom/2015/7211/0/07518455",
"title": "Sparse Autoencoder for Facial Expression Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/uic-atc-scalcom/2015/07518455/12OmNzBOi0t",
"parentPublication": {
"id": "proceedings/uic-atc-scalcom/2015/7211/0",
"title": "2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2021/3176/0/09666959",
"title": "Expression-Latent-Space-guided GAN for Facial Expression Animation based on Discrete Labels",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2021/09666959/1A6BAUodCMM",
"parentPublication": {
"id": "proceedings/fg/2021/3176/0",
"title": "2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2022/0915/0/091500c773",
"title": "Detection and Localization of Facial Expression Manipulations",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2022/091500c773/1B13Yu34wYE",
"parentPublication": {
"id": "proceedings/wacv/2022/0915/0",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2022/0915/0/091500c334",
"title": "Information Bottlenecked Variational Autoencoder for Disentangled 3D Facial Expression Modelling",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2022/091500c334/1B13eBUDbtC",
"parentPublication": {
"id": "proceedings/wacv/2022/0915/0",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200j660",
"title": "Learning Facial Representations from the Cycle-consistency of Face",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200j660/1BmHhWJPZGE",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09756299",
"title": "Semantically Disentangled Variational Autoencoder for Modeling 3D Facial Details",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09756299/1CvQiJgja2k",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2022/04/09854155",
"title": "Disentangling Identity and Pose for Facial Expression Recognition",
"doi": null,
"abstractUrl": "/journal/ta/2022/04/09854155/1FJ0DrohD4k",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412172",
"title": "Facial Expression Recognition By Using a Disentangled Identity-Invariant Expression Representation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412172/1tmhVk3jVhC",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900g755",
"title": "Learning a Facial Expression Embedding Disentangled from Identity",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900g755/1yeItVho9Pi",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1hQqfuoOyHu",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"acronym": "iccv",
"groupId": "1000149",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1hVlh0SqiPe",
"doi": "10.1109/ICCV.2019.00952",
"title": "Photo-Realistic Facial Details Synthesis From Single Image",
"normalizedTitle": "Photo-Realistic Facial Details Synthesis From Single Image",
"abstract": "We present a single-image 3D face synthesis technique that can handle challenging facial expressions while recovering fine geometric details. Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis. On proxy generation, we conduct emotion prediction to determine a new expression-informed proxy. On detail synthesis, we present a Deep Facial Detail Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs both geometry and appearance loss functions. For geometry, we capture 366 high-quality 3D scans from 122 different subjects under 3 facial expressions. For appearance, we use additional 163K in-the-wild face images and apply image-based rendering to accommodate lighting variations. Comprehensive experiments demonstrate that our framework can produce high-quality 3D faces with realistic details under challenging facial expressions.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a single-image 3D face synthesis technique that can handle challenging facial expressions while recovering fine geometric details. Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis. On proxy generation, we conduct emotion prediction to determine a new expression-informed proxy. On detail synthesis, we present a Deep Facial Detail Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs both geometry and appearance loss functions. For geometry, we capture 366 high-quality 3D scans from 122 different subjects under 3 facial expressions. For appearance, we use additional 163K in-the-wild face images and apply image-based rendering to accommodate lighting variations. Comprehensive experiments demonstrate that our framework can produce high-quality 3D faces with realistic details under challenging facial expressions.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present a single-image 3D face synthesis technique that can handle challenging facial expressions while recovering fine geometric details. Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis. On proxy generation, we conduct emotion prediction to determine a new expression-informed proxy. On detail synthesis, we present a Deep Facial Detail Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs both geometry and appearance loss functions. For geometry, we capture 366 high-quality 3D scans from 122 different subjects under 3 facial expressions. For appearance, we use additional 163K in-the-wild face images and apply image-based rendering to accommodate lighting variations. Comprehensive experiments demonstrate that our framework can produce high-quality 3D faces with realistic details under challenging facial expressions.",
"fno": "480300j428",
"keywords": [
"Computational Geometry",
"Emotion Recognition",
"Face Recognition",
"Neural Nets",
"Rendering Computer Graphics",
"Unsupervised Learning",
"Photo Realistic Facial Details Synthesis",
"Single Image 3 D Face Synthesis Technique",
"Facial Expressions",
"Fine Geometric Details",
"Expression Analysis",
"Proxy Face Geometry Generation",
"Supervised Learning",
"Unsupervised Learning",
"Facial Detail Synthesis",
"Proxy Generation",
"Expression Informed Proxy",
"Deep Facial Detail Net",
"Conditional Generative Adversarial Net",
"Appearance Loss Functions",
"High Quality 3 D Scans",
"High Quality 3 D Faces",
"Realistic Details",
"163 K In The Wild Face Images",
"Three Dimensional Displays",
"Face",
"Geometry",
"Shape",
"Cameras",
"Image Reconstruction",
"Two Dimensional Displays"
],
"authors": [
{
"affiliation": "shanghaitech",
"fullName": "Anpei Chen",
"givenName": "Anpei",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "ShanghaiTech University",
"fullName": "Zhang Chen",
"givenName": "Zhang",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghaitech University",
"fullName": "Guli Zhang",
"givenName": "Guli",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Edinburgh Napier University and Disney Research",
"fullName": "Kenny Mitchell",
"givenName": "Kenny",
"surname": "Mitchell",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shanghai Tech University",
"fullName": "Jingyi Yu",
"givenName": "Jingyi",
"surname": "Yu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-10-01T00:00:00",
"pubType": "proceedings",
"pages": "9428-9438",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-4803-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "480300j418",
"articleId": "1hQqjtAjgsM",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "480300j439",
"articleId": "1hVlATymDhS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2017/1032/0/1032f439",
"title": "Realistic Dynamic Facial Textures from a Single Image Using GANs",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032f439/12OmNBJeyIt",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032b031",
"title": "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032b031/12OmNCd2rI2",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2017/1034/0/1034a777",
"title": "Realtime Dynamic 3D Facial Reconstruction for Monocular Video In-the-Wild",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2017/1034a777/12OmNxaNGhz",
"parentPublication": {
"id": "proceedings/iccvw/2017/1034/0",
"title": "2017 IEEE International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2017/0457/0/0457f553",
"title": "Learning Detailed Face Reconstruction from a Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457f553/12OmNxvO05B",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000d877",
"title": "Modeling Facial Geometry Using Compositional VAEs",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000d877/17D45WwsQ8Y",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09756299",
"title": "Semantically Disentangled Variational Autoencoder for Modeling 3D Facial Details",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09756299/1CvQiJgja2k",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2020/1331/0/09102811",
"title": "Expression-Aware Face Reconstruction Via A Dual-Stream Network",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2020/09102811/1kwr15w4dQQ",
"parentPublication": {
"id": "proceedings/icme/2020/1331/0",
"title": "2020 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h707",
"title": "Deep 3D Portrait From a Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h707/1m3nijIcYta",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800a737",
"title": "Lightweight Photometric Stereo for Facial Details Recovery",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800a737/1m3nsjiR8Ck",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2021/2688/0/268800a815",
"title": "SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2021/268800a815/1zWE94Zh1Ru",
"parentPublication": {
"id": "proceedings/3dv/2021/2688/0",
"title": "2021 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1m3n9N02qgE",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1m3oiUnuaIM",
"doi": "10.1109/CVPR42600.2020.00794",
"title": "Guided Variational Autoencoder for Disentanglement Learning",
"normalizedTitle": "Guided Variational Autoencoder for Disentanglement Learning",
"abstract": "We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.",
"fno": "716800h917",
"keywords": [
"Decoding",
"Image Representation",
"Unsupervised Learning",
"Variational Techniques",
"Guided Variational Autoencoder",
"Guided VAE",
"Controllable Generative Model",
"Latent Representation Disentanglement Learning",
"Supervised Strategy",
"Enhanced Modeling",
"Controlling Capability",
"Vanilla VAE",
"Unsupervised Strategy",
"VAE Learning",
"Latent Geometric Transformation",
"Latent Variables",
"General Representation Learning Task",
"Meta Learning",
"Principal Component Analysis",
"Task Analysis",
"Decoding",
"Training",
"Gallium Nitride",
"Standards",
"Image Reconstruction"
],
"authors": [
{
"affiliation": "Tsinghua University; UC San Diego",
"fullName": "Zheng Ding",
"givenName": "Zheng",
"surname": "Ding",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "UC San Diego",
"fullName": "Yifan Xu",
"givenName": "Yifan",
"surname": "Xu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "UC San Diego",
"fullName": "Weijian Xu",
"givenName": "Weijian",
"surname": "Xu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "UC San Diego",
"fullName": "Gaurav Parmar",
"givenName": "Gaurav",
"surname": "Parmar",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Qualcomm, Inc.",
"fullName": "Yang Yang",
"givenName": "Yang",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Qualcomm, Inc.; University of Amsterdam",
"fullName": "Max Welling",
"givenName": "Max",
"surname": "Welling",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "UC San Diego",
"fullName": "Zhuowen Tu",
"givenName": "Zhuowen",
"surname": "Tu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-06-01T00:00:00",
"pubType": "proceedings",
"pages": "7917-7926",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-7168-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "716800h907",
"articleId": "1m3o6YLlYAg",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "716800h927",
"articleId": "1m3o9E4qs7K",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2022/6946/0/694600s8709",
"title": "3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600s8709/1H0LamYZIhq",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2019/9552/0/955200b354",
"title": "Data Augmentation for Monaural Singing Voice Separation Based on Variational Autoencoder-Generative Adversarial Network",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2019/955200b354/1cdOQFsZ1WU",
"parentPublication": {
"id": "proceedings/icme/2019/9552/0",
"title": "2019 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2019/3014/0/301400a407",
"title": "Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2019/301400a407/1h81BOjzqgw",
"parentPublication": {
"id": "proceedings/icdar/2019/3014/0",
"title": "2019 International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300i180",
"title": "Geometric Disentanglement for Generative Latent Shape Models",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300i180/1hQqibv9Neg",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300c979",
"title": "Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300c979/1hVlR8YbfUc",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093319",
"title": "Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093319/1jPbE7f7Ro4",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2020/3079/0/307900a117",
"title": "Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2020/307900a117/1kecHVv98vm",
"parentPublication": {
"id": "proceedings/fg/2020/3079/0/",
"title": "2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) (FG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150585",
"title": "Cross-modal Variational Alignment of Latent Spaces",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150585/1lPHxi9DP1u",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h975",
"title": "Joint Training of Variational Auto-Encoder and Latent Energy-Based Model",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h975/1m3oeVqbpsI",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412531",
"title": "Epitomic Variational Graph Autoencoder",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412531/1tmjpizgHWo",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1s645BaTzVu",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"acronym": "big-data",
"groupId": "1802964",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1s64JgJ8t32",
"doi": "10.1109/BigData50022.2020.9378242",
"title": "FSRGAN-DB: Super-resolution Reconstruction Based on Facial Prior Knowledge",
"normalizedTitle": "FSRGAN-DB: Super-resolution Reconstruction Based on Facial Prior Knowledge",
"abstract": "Face super-resolution (SR) reconstruction is a method of reconstructing a high-resolution (HR) face image from a low-resolution (LR) face image with more facial details. How-ever, most of the SR methods do not account for facial structures and suffer from loss of face details. In this paper, we propose a method that explicitly incorporates structural information of faces into the face super resolution network. We firstly use Super-Resolution Generative Adversarial Network(SRGAN) as the basic network and improve it by replacing residual blocks with dense blocks(SRGAN-DB). Then, we use the heat map of facial key points to describe the facial structure and the trend of facial contours. When the resolution of low-resolution images is very low, it is difficult to obtain fine facial features. But it is easy to get the information of facial key points. Specifically, the LR image is sent to two network branches, one obtains the fine features of the image, and the other generates a heat map of the facial key point positions. In order to enhance the feature expression of facial key points information, we convert the facial key points heat map into a binary image and connect it to the face fine encoder network. Extensive experiments prove that the proposed method is superior to existing network methods to face super-resolution reconstruction.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Face super-resolution (SR) reconstruction is a method of reconstructing a high-resolution (HR) face image from a low-resolution (LR) face image with more facial details. How-ever, most of the SR methods do not account for facial structures and suffer from loss of face details. In this paper, we propose a method that explicitly incorporates structural information of faces into the face super resolution network. We firstly use Super-Resolution Generative Adversarial Network(SRGAN) as the basic network and improve it by replacing residual blocks with dense blocks(SRGAN-DB). Then, we use the heat map of facial key points to describe the facial structure and the trend of facial contours. When the resolution of low-resolution images is very low, it is difficult to obtain fine facial features. But it is easy to get the information of facial key points. Specifically, the LR image is sent to two network branches, one obtains the fine features of the image, and the other generates a heat map of the facial key point positions. In order to enhance the feature expression of facial key points information, we convert the facial key points heat map into a binary image and connect it to the face fine encoder network. Extensive experiments prove that the proposed method is superior to existing network methods to face super-resolution reconstruction.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Face super-resolution (SR) reconstruction is a method of reconstructing a high-resolution (HR) face image from a low-resolution (LR) face image with more facial details. How-ever, most of the SR methods do not account for facial structures and suffer from loss of face details. In this paper, we propose a method that explicitly incorporates structural information of faces into the face super resolution network. We firstly use Super-Resolution Generative Adversarial Network(SRGAN) as the basic network and improve it by replacing residual blocks with dense blocks(SRGAN-DB). Then, we use the heat map of facial key points to describe the facial structure and the trend of facial contours. When the resolution of low-resolution images is very low, it is difficult to obtain fine facial features. But it is easy to get the information of facial key points. Specifically, the LR image is sent to two network branches, one obtains the fine features of the image, and the other generates a heat map of the facial key point positions. In order to enhance the feature expression of facial key points information, we convert the facial key points heat map into a binary image and connect it to the face fine encoder network. Extensive experiments prove that the proposed method is superior to existing network methods to face super-resolution reconstruction.",
"fno": "09378242",
"keywords": [
"Face Recognition",
"Image Reconstruction",
"Image Resolution",
"Low Resolution Face Image",
"Facial Details",
"SR Methods",
"Facial Structure",
"Face Details",
"Structural Information",
"Face Super Resolution Network",
"Basic Network",
"SRGAN DB",
"Facial Contours",
"Low Resolution Images",
"Fine Facial Features",
"LR Image",
"Network Branches",
"Facial Key Point Positions",
"Facial Key Points Information",
"Facial Key Points Heat Map",
"Binary Image",
"Face Fine Encoder Network",
"Network Methods",
"FSRGAN DB",
"Face Super Resolution Reconstruction",
"High Resolution Face Image",
"Heating Systems",
"Knowledge Engineering",
"Superresolution",
"Big Data",
"Market Research",
"Faces",
"Image Reconstruction",
"Super Resolution",
"Facial Prior Knowledge",
"Facial Key Points",
"Heat Map",
"Dense Blocks"
],
"authors": [
{
"affiliation": "Xiamen University of Technology,College of Computer and Information Engineering,Xiamen,China",
"fullName": "Wengang Zhou",
"givenName": "Wengang",
"surname": "Zhou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xiamen University of Technology,College of Computer and Information Engineering,Xiamen,China",
"fullName": "Chaoqun Hong",
"givenName": "Chaoqun",
"surname": "Hong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xiamen University of Technology,College of Computer and Information Engineering,Xiamen,China",
"fullName": "Xiaodong Wang",
"givenName": "Xiaodong",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xiamen University of Technology,College of Computer and Information Engineering,Xiamen,China",
"fullName": "Zhiqiang Zeng",
"givenName": "Zhiqiang",
"surname": "Zeng",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "big-data",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-12-01T00:00:00",
"pubType": "proceedings",
"pages": "3380-3386",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-6251-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09378341",
"articleId": "1s64PTPpAOc",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09377918",
"articleId": "1s657TsEKXu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2006/2521/4/252140342",
"title": "Super-resolution Restoration of Facial Images in Video",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2006/252140342/12OmNyeECu7",
"parentPublication": {
"id": "proceedings/icpr/2006/2521/4",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2008/2174/0/04761426",
"title": "Face super-resolution using 8-connected Markov Random Fields with embedded prior",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2008/04761426/12OmNzTppzU",
"parentPublication": {
"id": "proceedings/icpr/2008/2174/0",
"title": "ICPR 2008 19th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000c492",
"title": "FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000c492/17D45WXIkCH",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000a109",
"title": "Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000a109/17D45WwsQ8V",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859616",
"title": "RCNet: Recurrent Collaboration Network Guided by Facial Priors for Face Super-Resolution",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859616/1G9Eqh4agik",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/04/09887996",
"title": "Image Super-Resolution via Iterative Refinement",
"doi": null,
"abstractUrl": "/journal/tp/2023/04/09887996/1GBRjTq8Nag",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093399",
"title": "Component Attention Guided Face Super-Resolution Network: CAGFace",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093399/1jPbzBQruqQ",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150789",
"title": "MSFSR: A Multi-Stage Face Super-Resolution with Accurate Facial Representation via Enhanced Facial Boundaries",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150789/1lPHnkWB4vS",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09413117",
"title": "Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09413117/1tmj6A9WzG8",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/12/09591403",
"title": "Face Restoration via Plug-and-Play 3D Facial Priors",
"doi": null,
"abstractUrl": "/journal/tp/2022/12/09591403/1y2FdZSfPlm",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzVXNJa",
"title": "Convergence Information Technology, International Conference on",
"acronym": "iccit",
"groupId": "1001590",
"volume": "2",
"displayVolume": "2",
"year": "2008",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNwE9OJC",
"doi": "10.1109/ICCIT.2008.135",
"title": "Performance Evaluation of Teeth Image Recognition System Based on Difference Image Entropy",
"normalizedTitle": "Performance Evaluation of Teeth Image Recognition System Based on Difference Image Entropy",
"abstract": "In this paper, we propose improved Difference Image Entropy (herein after, DIE)-based teeth recognition system and input image selection method. The DIE estimation module computes the DIE coefficient reflecting histogram levels have peak positions from -255 to +255, after obtains gray scaled-difference image from input teeth image and average teeth image on random-collected reference images. For performance evaluation of DIE-based teeth image recognition system including in-put image selection method, we implemented individual teeth images-based teeth image recognition system using K-NN with PCA and 2D-DCT-based EHMM pattern recognition algorithms, and then they are coupled with suggested DIE-based in-put image selection method. After that we inspect availability and validity of the application by various experiments using DIE threshold values from 6.9 to 7.3 for suitable teeth image selection. In experimental results, the suggested teeth recognition system shows approximately from 3% to 15% improved recognition performance than traditional image-based biometric algorithms / methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we propose improved Difference Image Entropy (herein after, DIE)-based teeth recognition system and input image selection method. The DIE estimation module computes the DIE coefficient reflecting histogram levels have peak positions from -255 to +255, after obtains gray scaled-difference image from input teeth image and average teeth image on random-collected reference images. For performance evaluation of DIE-based teeth image recognition system including in-put image selection method, we implemented individual teeth images-based teeth image recognition system using K-NN with PCA and 2D-DCT-based EHMM pattern recognition algorithms, and then they are coupled with suggested DIE-based in-put image selection method. After that we inspect availability and validity of the application by various experiments using DIE threshold values from 6.9 to 7.3 for suitable teeth image selection. In experimental results, the suggested teeth recognition system shows approximately from 3% to 15% improved recognition performance than traditional image-based biometric algorithms / methods.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we propose improved Difference Image Entropy (herein after, DIE)-based teeth recognition system and input image selection method. The DIE estimation module computes the DIE coefficient reflecting histogram levels have peak positions from -255 to +255, after obtains gray scaled-difference image from input teeth image and average teeth image on random-collected reference images. For performance evaluation of DIE-based teeth image recognition system including in-put image selection method, we implemented individual teeth images-based teeth image recognition system using K-NN with PCA and 2D-DCT-based EHMM pattern recognition algorithms, and then they are coupled with suggested DIE-based in-put image selection method. After that we inspect availability and validity of the application by various experiments using DIE threshold values from 6.9 to 7.3 for suitable teeth image selection. In experimental results, the suggested teeth recognition system shows approximately from 3% to 15% improved recognition performance than traditional image-based biometric algorithms / methods.",
"fno": "3407c967",
"keywords": [
"Difference Image Entropy",
"Teeth Image Recognition",
"Input Image Selection"
],
"authors": [
{
"affiliation": null,
"fullName": "Jong-Bae Jeon",
"givenName": "Jong-Bae",
"surname": "Jeon",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jung-Hyun Kim",
"givenName": "Jung-Hyun",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jun-Ho Yoon",
"givenName": "Jun-Ho",
"surname": "Yoon",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Kwang-Seok Hong",
"givenName": "Kwang-Seok",
"surname": "Hong",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccit",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2008-11-01T00:00:00",
"pubType": "proceedings",
"pages": "967-972",
"year": "2008",
"issn": null,
"isbn": "978-0-7695-3407-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "3407c961",
"articleId": "12OmNzEVRVi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "3407c973",
"articleId": "12OmNzE54LT",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ihmsc/2010/4151/1/4151a271",
"title": "A Teeth Identification Method Based on Fuzzy Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/ihmsc/2010/4151a271/12OmNqI04Ge",
"parentPublication": {
"id": "proceedings/ihmsc/2010/4151/1",
"title": "Intelligent Human-Machine Systems and Cybernetics, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icoip/2010/4252/2/4252b645",
"title": "The Teeth Image Recognition Using HDM and the Turn Point",
"doi": null,
"abstractUrl": "/proceedings-article/icoip/2010/4252b645/12OmNx3HI7K",
"parentPublication": {
"id": null,
"title": null,
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2012/4637/0/4637a702",
"title": "The teeth occlusal view image recognition using extended HDM",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2012/4637a702/12OmNy6Zs2a",
"parentPublication": {
"id": "proceedings/icicta/2012/4637/0",
"title": "Intelligent Computation Technology and Automation, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ism/2012/4875/0/4875a145",
"title": "A New Approach to Teeth Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2012/4875a145/12OmNzlUKwe",
"parentPublication": {
"id": "proceedings/ism/2012/4875/0",
"title": "2012 IEEE International Symposium on Multimedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/2018/9264/0/926400a400",
"title": "Deep Instance Segmentation of Teeth in Panoramic X-Ray Images",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2018/926400a400/17D45W1Oa4w",
"parentPublication": {
"id": "proceedings/sibgrapi/2018/9264/0",
"title": "2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669660",
"title": "Analysis on Teeth Occlusion Distribution Based on Segmentation and Registration Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669660/1A9WeM2U9RC",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09720214",
"title": "TeethGNN: Semantic 3D Teeth Segmentation with Graph Neural Networks",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09720214/1BefbMXPO3C",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754991",
"title": "OrthoAligner: Image-based Teeth Alignment Prediction via Latent Style Manipulation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754991/1CubHSuE8bm",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956708",
"title": "Automatic teeth segmentation on panoramic X-rays using deep neural networks",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956708/1IHqmXCw89O",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismvl/2020/5406/0/540600a016",
"title": "Automatic Teeth Recognition in Dental X-Ray Images Using Transfer Learning Based Faster R-CNN",
"doi": null,
"abstractUrl": "/proceedings-article/ismvl/2020/540600a016/1qciaA2XbHy",
"parentPublication": {
"id": "proceedings/ismvl/2020/5406/0",
"title": "2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNrkjVb5",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"acronym": "aqtr",
"groupId": "1001746",
"volume": "2",
"displayVolume": "2",
"year": "2006",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzahc8g",
"doi": "10.1109/AQTR.2006.254674",
"title": "Numerical Simulation of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"normalizedTitle": "Numerical Simulation of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"abstract": "Continuing the paper (Colosi et al., 2006), \"Analogical modeling of periodontal stress distribution during orthodontic tipping of single rooted teeth\", this paper presents significant aspects regarding the numerical integration of a second order nonlinear differential equation which constitutes an original analogical model variant of uncontrolled orthodontic tipping for single rooted teeth. The corresponding logical scheme is presented and some obtained results are interpreted",
"abstracts": [
{
"abstractType": "Regular",
"content": "Continuing the paper (Colosi et al., 2006), \"Analogical modeling of periodontal stress distribution during orthodontic tipping of single rooted teeth\", this paper presents significant aspects regarding the numerical integration of a second order nonlinear differential equation which constitutes an original analogical model variant of uncontrolled orthodontic tipping for single rooted teeth. The corresponding logical scheme is presented and some obtained results are interpreted",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Continuing the paper (Colosi et al., 2006), \"Analogical modeling of periodontal stress distribution during orthodontic tipping of single rooted teeth\", this paper presents significant aspects regarding the numerical integration of a second order nonlinear differential equation which constitutes an original analogical model variant of uncontrolled orthodontic tipping for single rooted teeth. The corresponding logical scheme is presented and some obtained results are interpreted",
"fno": "04022997",
"keywords": [
"Dentistry",
"Integration",
"Nonlinear Differential Equations",
"Numerical Analysis",
"Orthodontic Force",
"Orthodontic Tipping",
"Numerical Simulation",
"Nonlinear Differential Equation",
"Taylor Series",
"Periodontal Stress Distribution",
"Single Rooted Teeth",
"Numerical Integration",
"Analogical Model",
"Numerical Simulation",
"Stress",
"Teeth",
"Differential Equations",
"Calculus",
"Taylor Series",
"Finite Wordlength Effects",
"Polynomials",
"Orthodontic Force",
"Orthodontic Tipping",
"Tooth",
"Numerical Simulation",
"Nonlinear Differential Equation",
"Taylor Series"
],
"authors": [
{
"affiliation": "University of Medicine and Pharmacy, 6 Pasteur Street, Cluj-Napoca, Romania",
"fullName": "H. A. Colosi",
"givenName": "H. A.",
"surname": "Colosi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Technical University, 25 Baritiu Street, Cluj-Napoca, Romania",
"fullName": "M. N. Roman",
"givenName": "M. N.",
"surname": "Roman",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Medicine and Pharmacy, 6 Pasteur Street, Cluj-Napoca, Romania",
"fullName": "A. Achimas Cadariu",
"givenName": "A. Achimas",
"surname": "Cadariu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "aqtr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2006-05-01T00:00:00",
"pubType": "proceedings",
"pages": "430-432",
"year": "2006",
"issn": null,
"isbn": "1-4244-0360-X",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "04022983",
"articleId": "12OmNzBOhPW",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "04022984",
"articleId": "12OmNviZliU",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/aqtr/2010/6724/1/05520892",
"title": "Method of numerical simulation for the air pollution dispersion",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2010/05520892/12OmNB0nWbT",
"parentPublication": {
"id": "proceedings/aqtr/2010/6724/1",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aqtr/2006/0360/2/04023003",
"title": "Necessary Preliminaries for the Modeling and Numerical Simulation of the Effects of Anterior Teeth Implantation on Complete Denture Stability",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2006/04023003/12OmNxXUhVB",
"parentPublication": {
"id": "proceedings/aqtr/2006/0360/2",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2012/4637/0/4637a702",
"title": "The teeth occlusal view image recognition using extended HDM",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2012/4637a702/12OmNy6Zs2a",
"parentPublication": {
"id": "proceedings/icicta/2012/4637/0",
"title": "Intelligent Computation Technology and Automation, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmtma/2009/3583/2/3583b895",
"title": "Stress Analysis and Optimization of Gear Teeth",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2009/3583b895/12OmNyoAAck",
"parentPublication": {
"id": "proceedings/icmtma/2009/3583/2",
"title": "2009 International Conference on Measuring Technology and Mechatronics Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aqtr/2006/0360/2/04022996",
"title": "Analogical Model of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2006/04022996/12OmNzt0IuX",
"parentPublication": {
"id": "proceedings/aqtr/2006/0360/2",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669660",
"title": "Analysis on Teeth Occlusion Distribution Based on Segmentation and Registration Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669660/1A9WeM2U9RC",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754991",
"title": "OrthoAligner: Image-based Teeth Alignment Prediction via Latent Style Manipulation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754991/1CubHSuE8bm",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icceai/2021/3960/0/396000a259",
"title": "Application of three-dimensional digital modeling of teeth and jaws in orthodontics teaching",
"doi": null,
"abstractUrl": "/proceedings-article/icceai/2021/396000a259/1xqyHh0kEi4",
"parentPublication": {
"id": "proceedings/icceai/2021/3960/0",
"title": "2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNrkjVb5",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"acronym": "aqtr",
"groupId": "1001746",
"volume": "2",
"displayVolume": "2",
"year": "2006",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzt0IuX",
"doi": "10.1109/AQTR.2006.254673",
"title": "Analogical Model of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"normalizedTitle": "Analogical Model of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"abstract": "The paper presents key elements of an original modeling method of dynamic stress distribution across the root surface of single rooted teeth, during uncontrolled orthodontic tipping. A nonlinear differential equation has been proposed as an approximation of equilibrium between different components of mechanical work during orthodontic movement of the tooth",
"abstracts": [
{
"abstractType": "Regular",
"content": "The paper presents key elements of an original modeling method of dynamic stress distribution across the root surface of single rooted teeth, during uncontrolled orthodontic tipping. A nonlinear differential equation has been proposed as an approximation of equilibrium between different components of mechanical work during orthodontic movement of the tooth",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The paper presents key elements of an original modeling method of dynamic stress distribution across the root surface of single rooted teeth, during uncontrolled orthodontic tipping. A nonlinear differential equation has been proposed as an approximation of equilibrium between different components of mechanical work during orthodontic movement of the tooth",
"fno": "04022996",
"keywords": [
"Biomechanics",
"Dentistry",
"Nonlinear Differential Equations",
"Orthodontic Force",
"Orthodontic Tipping",
"Rotation Center",
"Analogical Model",
"Nonlinear Differential Equation",
"Periodontal Stress Distribution",
"Single Rooted Teeth",
"Dynamic Stress Distribution",
"Stress",
"Teeth",
"Bones",
"Solid Modeling",
"Differential Equations",
"Ligaments",
"Sockets",
"Home Appliances",
"Force Control",
"Mathematical Model",
"Orthodontic Force",
"Orthodontic Tipping",
"Tooth",
"Rotation Center",
"Analogical Model",
"Nonlinear Differential Equation"
],
"authors": [
{
"affiliation": "University of Medicine and Pharmacy, 6 Pasteur Street, Cluj-Napoca, Romania",
"fullName": "H. A. Colosi",
"givenName": "H. A.",
"surname": "Colosi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Technical University",
"fullName": "M. N. Roman",
"givenName": "M. N.",
"surname": "Roman",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Medicine and Pharmacy, 6 Pasteur Street, Cluj-Napoca, Romania",
"fullName": "A. Achimas Cadariu",
"givenName": "A. Achimas",
"surname": "Cadariu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "aqtr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2006-05-01T00:00:00",
"pubType": "proceedings",
"pages": "426-429",
"year": "2006",
"issn": null,
"isbn": "1-4244-0360-X",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "04022982",
"articleId": "12OmNzZmZxt",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "04022983",
"articleId": "12OmNzBOhPW",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icmtma/2009/3583/2/3583b895",
"title": "Stress Analysis and Optimization of Gear Teeth",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2009/3583b895/12OmNyoAAck",
"parentPublication": {
"id": "proceedings/icmtma/2009/3583/2",
"title": "2009 International Conference on Measuring Technology and Mechatronics Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ism/2012/4875/0/4875a092",
"title": "Automatic Classification of Teeth in Bitewing Dental Images Using OLPP",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2012/4875a092/12OmNyr8YlK",
"parentPublication": {
"id": "proceedings/ism/2012/4875/0",
"title": "2012 IEEE International Symposium on Multimedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aqtr/2006/0360/2/04022997",
"title": "Numerical Simulation of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2006/04022997/12OmNzahc8g",
"parentPublication": {
"id": "proceedings/aqtr/2006/0360/2",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccsee/2012/4647/1/4647a080",
"title": "Study on Minimum Teeth without Undercutting of the Stub Tooth Involute Gears Based on VB",
"doi": null,
"abstractUrl": "/proceedings-article/iccsee/2012/4647a080/12OmNzgwmRP",
"parentPublication": {
"id": "proceedings/iccsee/2012/4647/2",
"title": "Computer Science and Electronics Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ism/2012/4875/0/4875a145",
"title": "A New Approach to Teeth Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2012/4875a145/12OmNzlUKwe",
"parentPublication": {
"id": "proceedings/ism/2012/4875/0",
"title": "2012 IEEE International Symposium on Multimedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669660",
"title": "Analysis on Teeth Occlusion Distribution Based on Segmentation and Registration Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669660/1A9WeM2U9RC",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/chase/2021/3965/0/396500a092",
"title": "TeethVib: Monitoring Teeth Functional Occlusion Through Retainer Vibration Sensing",
"doi": null,
"abstractUrl": "/proceedings-article/chase/2021/396500a092/1AIMHukq4qQ",
"parentPublication": {
"id": "proceedings/chase/2021/3965/0",
"title": "2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09720214",
"title": "TeethGNN: Semantic 3D Teeth Segmentation with Graph Neural Networks",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09720214/1BefbMXPO3C",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismvl/2020/5406/0/540600a016",
"title": "Automatic Teeth Recognition in Dental X-Ray Images Using Transfer Learning Based Faster R-CNN",
"doi": null,
"abstractUrl": "/proceedings-article/ismvl/2020/540600a016/1qciaA2XbHy",
"parentPublication": {
"id": "proceedings/ismvl/2020/5406/0",
"title": "2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/10/09445658",
"title": "A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT",
"doi": null,
"abstractUrl": "/journal/tp/2022/10/09445658/1uaajNYaeQw",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1A9VchbY4Mw",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1A9WeM2U9RC",
"doi": "10.1109/BIBM52615.2021.9669660",
"title": "Analysis on Teeth Occlusion Distribution Based on Segmentation and Registration Algorithm",
"normalizedTitle": "Analysis on Teeth Occlusion Distribution Based on Segmentation and Registration Algorithm",
"abstract": "Occlusal contact status of teeth is a key indicator for orthodontic and periodontal disease treatment. Digital analysis includes tooth position recognition and occlusal contact distribution estimation. In this study, we propose a cascade two-stage point-wise network named Teeth Segmentation Network (TSegNet) based on self-attention mechanism to address teeth segmentation task. And a template-based registration method is proposed to analyze the status of teeth occlusion. In TSegNet, spatial and channel attention are used to improve the performance feature extraction. Template-registration-based occlusal distribution analysis method reduced the labeled number of training samples. To the best of our knowledge, it is the first study on occlusal contact analyzing by using computer-aided-diagnosis technique. Experiment results illustrate the effectiveness and robustness of our proposed method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Occlusal contact status of teeth is a key indicator for orthodontic and periodontal disease treatment. Digital analysis includes tooth position recognition and occlusal contact distribution estimation. In this study, we propose a cascade two-stage point-wise network named Teeth Segmentation Network (TSegNet) based on self-attention mechanism to address teeth segmentation task. And a template-based registration method is proposed to analyze the status of teeth occlusion. In TSegNet, spatial and channel attention are used to improve the performance feature extraction. Template-registration-based occlusal distribution analysis method reduced the labeled number of training samples. To the best of our knowledge, it is the first study on occlusal contact analyzing by using computer-aided-diagnosis technique. Experiment results illustrate the effectiveness and robustness of our proposed method.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Occlusal contact status of teeth is a key indicator for orthodontic and periodontal disease treatment. Digital analysis includes tooth position recognition and occlusal contact distribution estimation. In this study, we propose a cascade two-stage point-wise network named Teeth Segmentation Network (TSegNet) based on self-attention mechanism to address teeth segmentation task. And a template-based registration method is proposed to analyze the status of teeth occlusion. In TSegNet, spatial and channel attention are used to improve the performance feature extraction. Template-registration-based occlusal distribution analysis method reduced the labeled number of training samples. To the best of our knowledge, it is the first study on occlusal contact analyzing by using computer-aided-diagnosis technique. Experiment results illustrate the effectiveness and robustness of our proposed method.",
"fno": "09669660",
"keywords": [
"Dentistry",
"Diseases",
"Feature Extraction",
"Image Registration",
"Image Segmentation",
"Medical Image Processing",
"Registration Algorithm",
"Occlusal Contact Status",
"Orthodontic Disease Treatment",
"Periodontal Disease Treatment",
"Digital Analysis",
"Tooth Position Recognition",
"Occlusal Contact Distribution Estimation",
"Cascade Two Stage Point Wise Network",
"Self Attention Mechanism",
"Teeth Segmentation Task",
"Template Based Registration Method",
"Channel Attention",
"Template Registration Based Occlusal Distribution Analysis Method",
"Teeth Segmentation Network",
"T Seg Net",
"Training",
"Point Cloud Compression",
"Three Dimensional Displays",
"Estimation",
"Teeth",
"Feature Extraction",
"Robustness",
"Computer Aided Dentistry",
"Teeth Occlusion Discriminate",
"3 D Point Cloud",
"Segmentation",
"Registration"
],
"authors": [
{
"affiliation": "University of Electronic Science and Technology of China,Chengdu,China",
"fullName": "Zihan Cao",
"givenName": "Zihan",
"surname": "Cao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Electronic Science and Technology of China,Chengdu,China",
"fullName": "Xinwu Sun",
"givenName": "Xinwu",
"surname": "Sun",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Electronic Science and Technology of China,Chengdu,China",
"fullName": "Shasha Liu",
"givenName": "Shasha",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Capital Medical University,Beijing Stomatological Hospital,Beijing,China",
"fullName": "Gangyuan Chen",
"givenName": "Gangyuan",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Chinese Academy of Sciences,Beijing,China",
"fullName": "Yan Liu",
"givenName": "Yan",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Capital Medical University,Beijing Stomatological Hospital,Beijing,China",
"fullName": "Xinggang Liu",
"givenName": "Xinggang",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Capital Medical University,Beijing Stomatological Hospital,Beijing,China",
"fullName": "Dongxiang Zheng",
"givenName": "Dongxiang",
"surname": "Zheng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Electronic Science and Technology of China,Chengdu,China",
"fullName": "Ling Wang",
"givenName": "Ling",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-12-01T00:00:00",
"pubType": "proceedings",
"pages": "1266-1269",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-0126-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09669375",
"articleId": "1A9WkjpRzkk",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09669613",
"articleId": "1A9VR7N4Ydq",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ca/1995/7062/0/70620163",
"title": "Evaluation of human jaw articulation [computer animation]",
"doi": null,
"abstractUrl": "/proceedings-article/ca/1995/70620163/12OmNBV9IjS",
"parentPublication": {
"id": "proceedings/ca/1995/7062/0",
"title": "Computer Animation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2012/1611/0/06239249",
"title": "Realistic 3D reconstruction of the human teeth using shape from shading with shape priors",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2012/06239249/12OmNC0guzp",
"parentPublication": {
"id": "proceedings/cvprw/2012/1611/0",
"title": "2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ihmsc/2010/4151/1/4151a271",
"title": "A Teeth Identification Method Based on Fuzzy Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/ihmsc/2010/4151a271/12OmNqI04Ge",
"parentPublication": {
"id": "proceedings/ihmsc/2010/4151/1",
"title": "Intelligent Human-Machine Systems and Cybernetics, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icoip/2010/4252/2/4252b645",
"title": "The Teeth Image Recognition Using HDM and the Turn Point",
"doi": null,
"abstractUrl": "/proceedings-article/icoip/2010/4252b645/12OmNx3HI7K",
"parentPublication": {
"id": null,
"title": null,
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2012/4637/0/4637a702",
"title": "The teeth occlusal view image recognition using extended HDM",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2012/4637a702/12OmNy6Zs2a",
"parentPublication": {
"id": "proceedings/icicta/2012/4637/0",
"title": "Intelligent Computation Technology and Automation, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmtma/2009/3583/2/3583b895",
"title": "Stress Analysis and Optimization of Gear Teeth",
"doi": null,
"abstractUrl": "/proceedings-article/icmtma/2009/3583b895/12OmNyoAAck",
"parentPublication": {
"id": "proceedings/icmtma/2009/3583/2",
"title": "2009 International Conference on Measuring Technology and Mechatronics Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ism/2012/4875/0/4875a145",
"title": "A New Approach to Teeth Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2012/4875a145/12OmNzlUKwe",
"parentPublication": {
"id": "proceedings/ism/2012/4875/0",
"title": "2012 IEEE International Symposium on Multimedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/chase/2021/3965/0/396500a092",
"title": "TeethVib: Monitoring Teeth Functional Occlusion Through Retainer Vibration Sensing",
"doi": null,
"abstractUrl": "/proceedings-article/chase/2021/396500a092/1AIMHukq4qQ",
"parentPublication": {
"id": "proceedings/chase/2021/3965/0",
"title": "2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09720214",
"title": "TeethGNN: Semantic 3D Teeth Segmentation with Graph Neural Networks",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09720214/1BefbMXPO3C",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956708",
"title": "Automatic teeth segmentation on panoramic X-rays using deep neural networks",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956708/1IHqmXCw89O",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1m3n9N02qgE",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1m3ng5xOC08",
"doi": "10.1109/CVPR42600.2020.00618",
"title": "StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images",
"normalizedTitle": "StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images",
"abstract": "StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable face models (3DMMs) on the other hand offer control over the semantic parameters, but lack photorealism when rendered and only model the face interior, not other parts of a portrait image (hair, mouth interior, background). We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM. A new rigging network, is trained between the 3DMM's semantic parameters and StyleGAN's input. The network is trained in a self-supervised manner, without the need for manual annotations. At test time, our method generates portrait images with the photorealism of StyleGAN and provides explicit control over the 3D semantic parameters of the face.",
"abstracts": [
{
"abstractType": "Regular",
"content": "StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable face models (3DMMs) on the other hand offer control over the semantic parameters, but lack photorealism when rendered and only model the face interior, not other parts of a portrait image (hair, mouth interior, background). We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM. A new rigging network, is trained between the 3DMM's semantic parameters and StyleGAN's input. The network is trained in a self-supervised manner, without the need for manual annotations. At test time, our method generates portrait images with the photorealism of StyleGAN and provides explicit control over the 3D semantic parameters of the face.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable face models (3DMMs) on the other hand offer control over the semantic parameters, but lack photorealism when rendered and only model the face interior, not other parts of a portrait image (hair, mouth interior, background). We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM. A new rigging network, is trained between the 3DMM's semantic parameters and StyleGAN's input. The network is trained in a self-supervised manner, without the need for manual annotations. At test time, our method generates portrait images with the photorealism of StyleGAN and provides explicit control over the 3D semantic parameters of the face.",
"fno": "716800g141",
"keywords": [
"Face Recognition",
"Image Texture",
"Rendering Computer Graphics",
"Solid Modelling",
"3 D Control",
"Portrait Image",
"Photorealistic Portrait Images",
"Semantic Face Parameters",
"Three Dimensional Morphable Face Models",
"Hand Offer Control",
"Face Interior",
"Face Rig Like Control",
"Pretrained Fixed Style GAN",
"Rigging Network",
"3 DM Ms Semantic Parameters",
"Style GA Ns Input",
"Explicit Control",
"Face",
"Semantics",
"Training",
"Three Dimensional Displays",
"Image Reconstruction",
"Gallium Nitride",
"Lighting"
],
"authors": [
{
"affiliation": "MPI Informatics, Saarland Informatics Campus",
"fullName": "Ayush Tewari",
"givenName": "Ayush",
"surname": "Tewari",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "MPI Informatics, Saarland Informatics Campus",
"fullName": "Mohamed Elgharib",
"givenName": "Mohamed",
"surname": "Elgharib",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Technicolor",
"fullName": "Gaurav Bharaj",
"givenName": "Gaurav",
"surname": "Bharaj",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "MPI Informatics, Saarland Informatics Campus",
"fullName": "Florian Bernard",
"givenName": "Florian",
"surname": "Bernard",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "MPI Informatics, Saarland Informatics Campus",
"fullName": "Hans-Peter Seidel",
"givenName": "Hans-Peter",
"surname": "Seidel",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Valeo.ai",
"fullName": "Patrick Pérez",
"givenName": "Patrick",
"surname": "Pérez",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Stanford University",
"fullName": "Michael Zollhöfer",
"givenName": "Michael",
"surname": "Zollhöfer",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "MPI Informatics, Saarland Informatics Campus",
"fullName": "Christian Theobalt",
"givenName": "Christian",
"surname": "Theobalt",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-06-01T00:00:00",
"pubType": "proceedings",
"pages": "6141-6150",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-7168-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "716800g131",
"articleId": "1m3nhCYJ6mY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "716800g151",
"articleId": "1m3nhMPzShO",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tg/2020/08/08611145",
"title": "Portrait Relief Modeling from a Single Image",
"doi": null,
"abstractUrl": "/journal/tg/2020/08/08611145/17D45XDIXSX",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09784910",
"title": "DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09784910/1DPaE3QYx68",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600d419",
"title": "Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600d419/1H0N5arEqM8",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600h683",
"title": "Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600h683/1H0NNPChQsM",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300h193",
"title": "Deep Single-Image Portrait Relighting",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300h193/1hQqn2MnaNO",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300e431",
"title": "Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300e431/1hVlkO6d9HW",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/09141516",
"title": "FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/09141516/1lu2DsXmJ8s",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800f770",
"title": "Editing in Style: Uncovering the Local Semantics of GANs",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800f770/1m3nVX2Mpvq",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800i214",
"title": "Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800i214/1m3ncKO9WqQ",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h707",
"title": "Deep 3D Portrait From a Single Image",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h707/1m3nijIcYta",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1m3n9N02qgE",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1m3okyROwx2",
"doi": "10.1109/CVPR42600.2020.01411",
"title": "Adversarial Latent Autoencoders",
"normalizedTitle": "Adversarial Latent Autoencoders",
"abstract": "Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.",
"fno": "716800o4092",
"keywords": [
"Face Recognition",
"Image Reconstruction",
"Learning Artificial Intelligence",
"General Architecture",
"Leverage Recent Improvements",
"GAN Training Procedures",
"MLP Encoder",
"Style GAN Generator",
"Disentanglement Properties",
"1024 X 1024 Face Images",
"Adversarial Latent Autoencoders",
"Autoencoder Networks",
"Unsupervised Approaches",
"Generative Properties",
"Representational Properties",
"Encoder Generator Map",
"Disentangled Representations",
"Adversarial Latent Autoencoder",
"Gallium Nitride",
"Generators",
"Generative Adversarial Networks",
"Face",
"Computer Architecture",
"Training",
"Image Resolution"
],
"authors": [
{
"affiliation": "Lane Department of Computer Science and Electrical Engineering, West Virginia University, USA",
"fullName": "Stanislav Pidhorskyi",
"givenName": "Stanislav",
"surname": "Pidhorskyi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Lane Department of Computer Science and Electrical Engineering, West Virginia University, USA",
"fullName": "Donald A. Adjeroh",
"givenName": "Donald A.",
"surname": "Adjeroh",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Lane Department of Computer Science and Electrical Engineering, West Virginia University, USA",
"fullName": "Gianfranco Doretto",
"givenName": "Gianfranco",
"surname": "Doretto",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-06-01T00:00:00",
"pubType": "proceedings",
"pages": "14092-14101",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-7168-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "716800o4083",
"articleId": "1m3ofxnBETC",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "716800o4102",
"articleId": "1m3ng8Pl0go",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ictai/2018/7449/0/744900a242",
"title": "Sequence Generative Adversarial Network for Long Text Summarization",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2018/744900a242/17D45WIXbQb",
"parentPublication": {
"id": "proceedings/ictai/2018/7449/0",
"title": "2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000b498",
"title": "Duplex Generative Adversarial Network for Unsupervised Domain Adaptation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000b498/17D45WYQJ9i",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2018/3788/0/08545701",
"title": "Pyramid Embedded Generative Adversarial Network for Automated Font Generation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2018/08545701/17D45Wc1IJp",
"parentPublication": {
"id": "proceedings/icpr/2018/3788/0",
"title": "2018 24th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2018/3788/0/08545633",
"title": "Facial Attribute Editing by Latent Space Adversarial Variational Autoencoders",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2018/08545633/17D45XfSEVf",
"parentPublication": {
"id": "proceedings/icpr/2018/3788/0",
"title": "2018 24th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2019/1975/0/197500a263",
"title": "Improving Diversity of Image Captioning Through Variational Autoencoders and Adversarial Learning",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2019/197500a263/18j8JrjkFRm",
"parentPublication": {
"id": "proceedings/wacv/2019/1975/0",
"title": "2019 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdew/2019/0890/0/089000a161",
"title": "Collaborative Generative Adversarial Network for Recommendation Systems",
"doi": null,
"abstractUrl": "/proceedings-article/icdew/2019/089000a161/1bhJ8MxYi4g",
"parentPublication": {
"id": "proceedings/icdew/2019/0890/0",
"title": "2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300a221",
"title": "Generative Adversarial Networks for Extreme Learned Image Compression",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300a221/1hQqg054KNW",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2020/1331/0/09102904",
"title": "Text to Image Synthesis With Bidirectional Generative Adversarial Network",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2020/09102904/1kwr76JKhoc",
"parentPublication": {
"id": "proceedings/icme/2020/1331/0",
"title": "2020 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h796",
"title": "MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h796/1m3oneHfgTS",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/05/09290435",
"title": "Optimizing Latent Distributions for Non-Adversarial Generative Networks",
"doi": null,
"abstractUrl": "/journal/tp/2022/05/09290435/1prKHvxF2lW",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1wLcd132uKA",
"title": "2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)",
"acronym": "compsac",
"groupId": "1000143",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1wLcwLrusUw",
"doi": "10.1109/COMPSAC51774.2021.00240",
"title": "Shared-latent Variable Network Alignment",
"normalizedTitle": "Shared-latent Variable Network Alignment",
"abstract": "The increasing popularity and diversity of social media sites, has encouraged many people to participate in different online social networks to enjoy a variety of services. Linking the same users across different social networks, also known as social network alignment, is a critical task of great research challenges. Many existing works usually focus on finding a projection function from one subspace to another for network alignment, however, the projection functions proposed in their papers are independent and updated individually, which could not effectively exploit the non-parallel data, and yield inferior alignment performance. In this paper, we propose a Shared-latent Variable Network Alignment (SVNA) architecture to effectively exploit the non-parallel data for network alignment, and jointly train projection functions and decoders in a unified framework with the shared latent variable z. Specifically, SVNA first employs the graph convolutional networks to preserve the structural information of the network. By introducing the shared latent variable z, SVNA simultaneously integrates two projection functions and two decoders for jointly training. Both projection functions and decoders share the same latent space, therefore both projection directions can learn from the non-parallel data more effectively. Thereafter, SVNA utilizes the Generative Adversarial Networks (GANs) framework to further train the projection functions, and adopts a probability-based semi-supervised method to achieve the network alignment. Experiments on three real-world datasets show that SVNA generally outperforms the state-of-the-art methods in network alignment task.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The increasing popularity and diversity of social media sites, has encouraged many people to participate in different online social networks to enjoy a variety of services. Linking the same users across different social networks, also known as social network alignment, is a critical task of great research challenges. Many existing works usually focus on finding a projection function from one subspace to another for network alignment, however, the projection functions proposed in their papers are independent and updated individually, which could not effectively exploit the non-parallel data, and yield inferior alignment performance. In this paper, we propose a Shared-latent Variable Network Alignment (SVNA) architecture to effectively exploit the non-parallel data for network alignment, and jointly train projection functions and decoders in a unified framework with the shared latent variable z. Specifically, SVNA first employs the graph convolutional networks to preserve the structural information of the network. By introducing the shared latent variable z, SVNA simultaneously integrates two projection functions and two decoders for jointly training. Both projection functions and decoders share the same latent space, therefore both projection directions can learn from the non-parallel data more effectively. Thereafter, SVNA utilizes the Generative Adversarial Networks (GANs) framework to further train the projection functions, and adopts a probability-based semi-supervised method to achieve the network alignment. Experiments on three real-world datasets show that SVNA generally outperforms the state-of-the-art methods in network alignment task.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The increasing popularity and diversity of social media sites, has encouraged many people to participate in different online social networks to enjoy a variety of services. Linking the same users across different social networks, also known as social network alignment, is a critical task of great research challenges. Many existing works usually focus on finding a projection function from one subspace to another for network alignment, however, the projection functions proposed in their papers are independent and updated individually, which could not effectively exploit the non-parallel data, and yield inferior alignment performance. In this paper, we propose a Shared-latent Variable Network Alignment (SVNA) architecture to effectively exploit the non-parallel data for network alignment, and jointly train projection functions and decoders in a unified framework with the shared latent variable z. Specifically, SVNA first employs the graph convolutional networks to preserve the structural information of the network. By introducing the shared latent variable z, SVNA simultaneously integrates two projection functions and two decoders for jointly training. Both projection functions and decoders share the same latent space, therefore both projection directions can learn from the non-parallel data more effectively. Thereafter, SVNA utilizes the Generative Adversarial Networks (GANs) framework to further train the projection functions, and adopts a probability-based semi-supervised method to achieve the network alignment. Experiments on three real-world datasets show that SVNA generally outperforms the state-of-the-art methods in network alignment task.",
"fno": "246300b611",
"keywords": [
"Graph Theory",
"Learning Artificial Intelligence",
"Probability",
"Social Networking Online",
"Nonparallel Data",
"SVNA",
"Generative Adversarial Networks Framework",
"Projection Function",
"Network Alignment Task",
"Social Media Sites",
"Different Online Social Networks",
"Different Social Networks",
"Social Network Alignment",
"Inferior Alignment Performance",
"Shared Latent Variable Network Alignment Architecture",
"Train Projection Functions",
"Graph Convolutional Networks",
"Training",
"Social Networking Online",
"Computational Modeling",
"Conferences",
"Generative Adversarial Networks",
"Software",
"Decoding",
"Network Alignment",
"Adversarial Learning",
"Graph Convolutional Networks",
"Latent Variable"
],
"authors": [
{
"affiliation": "Beijing Institute of Technology,School of Computer Science,Beijing,China",
"fullName": "Degen Zhang",
"givenName": "Degen",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Institute of Technology,School of Computer Science,Beijing,China",
"fullName": "Xin Li",
"givenName": "Xin",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Institute of Technology,School of Computer Science,Beijing,China",
"fullName": "Linjing Lai",
"givenName": "Linjing",
"surname": "Lai",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "compsac",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-07-01T00:00:00",
"pubType": "proceedings",
"pages": "1611-1616",
"year": "2021",
"issn": "0730-3157",
"isbn": "978-1-6654-2463-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "246300b605",
"articleId": "1wLcCUyfnz2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "246300b617",
"articleId": "1wLctPkXJ8k",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2018/3788/0/08545633",
"title": "Facial Attribute Editing by Latent Space Adversarial Variational Autoencoders",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2018/08545633/17D45XfSEVf",
"parentPublication": {
"id": "proceedings/icpr/2018/3788/0",
"title": "2018 24th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754991",
"title": "OrthoAligner: Image-based Teeth Alignment Prediction via Latent Style Manipulation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754991/1CubHSuE8bm",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10016755",
"title": "FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10016755/1JTZYZSUiGc",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093322",
"title": "CrossNet: Latent Cross-Consistency for Unpaired Image Translation",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093322/1jPbjNBRn3y",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150306",
"title": "Latent Fingerprint Image Enhancement Based on Progressive Generative Adversarial Network",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150306/1lPHiGvS7Vm",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150585",
"title": "Cross-modal Variational Alignment of Latent Spaces",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150585/1lPHxi9DP1u",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800j240",
"title": "Interpreting the Latent Space of GANs for Semantic Face Editing",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800j240/1m3nRsHWYJa",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2020/6497/0/649700a258",
"title": "Mask2LFP: Mask-constrained Adversarial Latent Fingerprint Synthesis",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2020/649700a258/1olHA8aLAME",
"parentPublication": {
"id": "proceedings/cw/2020/6497/0",
"title": "2020 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377929",
"title": "Unsupervised Multiple Network Alignment with Multinominal GAN and Variational Inference",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377929/1s64YS7e9ri",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2021/4065/0/406500a125",
"title": "Application of Generative Adversarial Networks and Latent Space Exploration in Music Visualisation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2021/406500a125/1yBEZneGLug",
"parentPublication": {
"id": "proceedings/cw/2021/4065/0",
"title": "2021 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": [
{
"id": "1xx9v1W0M6c",
"videoExt": "mp4",
"videoType": {
"featured": false,
"recommended": false,
"sponsored": false,
"__typename": "VideoTypesType"
},
"article": {
"id": "1wLcwLrusUw",
"fno": "246300b611",
"issueNum": null,
"pubType": "proceedings",
"volume": "0",
"year": "2021",
"idPrefix": "compsac",
"doi": "10.1109/COMPSAC51774.2021.00240",
"title": "Shared-latent Variable Network Alignment",
"__typename": "ArticleType"
},
"channel": {
"id": "1xvX5qkErBu",
"title": "COMPSAC 2021",
"status": "1",
"featured": false,
"defaultVideoId": "1xvX5cz6Dtu",
"category": {
"id": "1xvX5lT1WiQ",
"title": "Proceeding",
"type": "proceeding",
"__typename": "VideoCategoryType"
},
"__typename": "VideoChannelType"
},
"year": "2021",
"title": "Shared-latent Variable Network Alignment",
"description": "The increasing popularity and diversity of social media sites, has encouraged many people to participate in different online social networks to enjoy a variety of services. Linking the same users across different social networks, also known as social network alignment, is a critical task of great research challenges. Many existing works usually focus on finding a projection function from one subspace to another for network alignment, however, the projection functions proposed in their papers are independent and updated individually, which could not effectively exploit the non-parallel data, and yield inferior alignment performance. In this paper, we propose a Shared-latent Variable Network Alignment (SVNA) architecture to effectively exploit the non-parallel data for network alignment, and jointly train projection functions and decoders in a unified framework with the shared latent variable z. Specifically, SVNA first employs the graph convolutional networks to preserve the structural information of the network. By introducing the shared latent variable z, SVNA simultaneously integrates two projection functions and two decoders for jointly training. Both projection functions and decoders share the same latent space, therefore both projection directions can learn from the non-parallel data more effectively. Thereafter, SVNA utilizes the Generative Adversarial Networks (GANs) framework to further train the projection functions, and adopts a probability-based semi-supervised method to achieve the network alignment. Experiments on three real-world datasets show that SVNA generally outperforms the state-of-the-art methods in network alignment task.",
"keywords": [
{
"id": "1xvX5I2jhAY",
"title": "Conferences",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xvX5PnBSes",
"title": "Software",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xvX82bYYco",
"title": "Computational modeling",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xvX9hjLzpe",
"title": "Training",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xw8h6BlKJW",
"title": "Generative adversarial networks",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xw8jHzTHeo",
"title": "Social networking (online)",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xx9va9f9rW",
"title": "Decoding",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xx9vcc7QAM",
"title": "Network Alignment",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xx9vfKXLMY",
"title": "Adversarial Learning",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xx9vibGLRe",
"title": "Graph Convolutional Networks",
"status": "1",
"__typename": "VideoKeywordsType"
},
{
"id": "1xx9vkqMdAQ",
"title": "Latent Variable",
"status": "1",
"__typename": "VideoKeywordsType"
}
],
"speakers": [
{
"firstName": "Degen",
"lastName": "Zhang",
"affiliation": "Beijing Institute of Technology,School of Computer Science,Beijing,China",
"__typename": "SpeakerType"
},
{
"firstName": "Xin",
"lastName": "Li",
"affiliation": "Beijing Institute of Technology,School of Computer Science,Beijing,China",
"__typename": "SpeakerType"
},
{
"firstName": "Linjing",
"lastName": "Lai",
"affiliation": "Beijing Institute of Technology,School of Computer Science,Beijing,China",
"__typename": "SpeakerType"
}
],
"created": "2021-10-08T00:00:00",
"updated": "2021-10-08T00:00:00",
"imageThumbnailUrl": "thumbnails/1xx9v1W0M6c.jpeg",
"runningTime": "00:08:06",
"aspectRatio": "16:9",
"metrics": {
"views": "0",
"likes": "0",
"__typename": "VideoMetricsType"
},
"notShowInVideoLib": false,
"__typename": "VideoType"
}
]
}
|
{
"proceeding": {
"id": "1xqyG9WHggU",
"title": "2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)",
"acronym": "icceai",
"groupId": "1843184",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1xqyHh0kEi4",
"doi": "10.1109/ICCEAI52939.2021.00051",
"title": "Application of three-dimensional digital modeling of teeth and jaws in orthodontics teaching",
"normalizedTitle": "Application of three-dimensional digital modeling of teeth and jaws in orthodontics teaching",
"abstract": "Orthodontics involves anatomy and physiology, growth and development, biomechanics, mechanical mechanics and other aspects, so it is difficult to learn and master such a large and complex theoretical knowledge. With the rapid development of digital technology, the three-dimensional digital model of teeth and jaws is more widely used. It can present the anatomical structure in three dimensions, and then combine with three-dimensional finite element method to simulate the force characteristics and movement mode of teeth. It is of great benefit for students to quickly and deeply understand the anatomical structure and orthodontic biomechanics. The purpose of this study is to explore the role of digital model in orthodontics teaching by constructing three-dimensional digital model of teeth and jaw and three-dimensional finite element analysis.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Orthodontics involves anatomy and physiology, growth and development, biomechanics, mechanical mechanics and other aspects, so it is difficult to learn and master such a large and complex theoretical knowledge. With the rapid development of digital technology, the three-dimensional digital model of teeth and jaws is more widely used. It can present the anatomical structure in three dimensions, and then combine with three-dimensional finite element method to simulate the force characteristics and movement mode of teeth. It is of great benefit for students to quickly and deeply understand the anatomical structure and orthodontic biomechanics. The purpose of this study is to explore the role of digital model in orthodontics teaching by constructing three-dimensional digital model of teeth and jaw and three-dimensional finite element analysis.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Orthodontics involves anatomy and physiology, growth and development, biomechanics, mechanical mechanics and other aspects, so it is difficult to learn and master such a large and complex theoretical knowledge. With the rapid development of digital technology, the three-dimensional digital model of teeth and jaws is more widely used. It can present the anatomical structure in three dimensions, and then combine with three-dimensional finite element method to simulate the force characteristics and movement mode of teeth. It is of great benefit for students to quickly and deeply understand the anatomical structure and orthodontic biomechanics. The purpose of this study is to explore the role of digital model in orthodontics teaching by constructing three-dimensional digital model of teeth and jaw and three-dimensional finite element analysis.",
"fno": "396000a259",
"keywords": [
"Biomechanics",
"Dentistry",
"Finite Element Analysis",
"Orthotics",
"Jaws",
"Orthodontics",
"Mechanical Mechanics",
"Large Knowledge",
"Complex Theoretical Knowledge",
"Digital Technology",
"Three Dimensional Digital Model",
"Anatomical Structure",
"Three Dimensional Finite Element Method",
"Orthodontic Biomechanics",
"Biomechanics",
"Solid Modeling",
"Analytical Models",
"Biological System Modeling",
"Education",
"Teeth",
"Anatomical Structure",
"Digital Modeling",
"Three Dimensional Finite Element",
"Orthodontics",
"Teaching Mold"
],
"authors": [
{
"affiliation": "School of Stomatology Stomatological Hospital Lanzhou University Gansu Province,China",
"fullName": "Jia Guo",
"givenName": "Jia",
"surname": "Guo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Stomatology Stomatological Hospital Lanzhou University Gansu Province,China",
"fullName": "Xusen Wang",
"givenName": "Xusen",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Stomatology Stomatological Hospital Lanzhou University Gansu Province,China",
"fullName": "Hongfang Zhao",
"givenName": "Hongfang",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Stomatology Stomatological Hospital Lanzhou University Gansu Province,China",
"fullName": "Junjie Wang",
"givenName": "Junjie",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icceai",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-08-01T00:00:00",
"pubType": "proceedings",
"pages": "259-261",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-3960-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "396000a254",
"articleId": "1xqyP0M7g0U",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "396000a262",
"articleId": "1xqyIRxYLYI",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icfcse/2011/1562/0/06041680",
"title": "Development and Application of Three-dimensional Digital Discus Based on MEMS Sensor",
"doi": null,
"abstractUrl": "/proceedings-article/icfcse/2011/06041680/12OmNAPBbhm",
"parentPublication": {
"id": "proceedings/icfcse/2011/1562/0",
"title": "2011 International Conference on Future Computer Science and Education",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icfcse/2011/1562/0/06041637",
"title": "A Novel Digital Spiked Shoes Design and Testing",
"doi": null,
"abstractUrl": "/proceedings-article/icfcse/2011/06041637/12OmNCbU35m",
"parentPublication": {
"id": "proceedings/icfcse/2011/1562/0",
"title": "2011 International Conference on Future Computer Science and Education",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ihmsc/2010/4151/1/4151a271",
"title": "A Teeth Identification Method Based on Fuzzy Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/ihmsc/2010/4151a271/12OmNqI04Ge",
"parentPublication": {
"id": "proceedings/ihmsc/2010/4151/1",
"title": "Intelligent Human-Machine Systems and Cybernetics, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccis/2010/4270/0/4270a420",
"title": "Study on Cervical Spine Stresses Based on Three-Dimensional Finite Element Method",
"doi": null,
"abstractUrl": "/proceedings-article/iccis/2010/4270a420/12OmNwCsdOJ",
"parentPublication": {
"id": "proceedings/iccis/2010/4270/0",
"title": "2010 International Conference on Computational and Information Sciences",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aqtr/2006/0360/2/04022996",
"title": "Analogical Model of Periodontal Stress Distribution during Orthodontic Tipping of Single Rooted Teeth",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2006/04022996/12OmNzt0IuX",
"parentPublication": {
"id": "proceedings/aqtr/2006/0360/2",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/lt/2016/02/07345566",
"title": "ToothPIC: An Interactive Application for Teaching Oral Anatomy",
"doi": null,
"abstractUrl": "/journal/lt/2016/02/07345566/13rRUxYrbIk",
"parentPublication": {
"id": "trans/lt",
"title": "IEEE Transactions on Learning Technologies",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754991",
"title": "OrthoAligner: Image-based Teeth Alignment Prediction via Latent Style Manipulation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754991/1CubHSuE8bm",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09933028",
"title": "Tooth Alignment Network Based on Landmark Constraints and Hierarchical Graph Structure",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09933028/1HVsnduN8e4",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2022/6819/0/09995297",
"title": "LeFUNet: UNet with Learnable Feature Connections for Teeth Identification and Segmentation in Dental Panoramic X-ray Images",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2022/09995297/1JC3u99qicM",
"parentPublication": {
"id": "proceedings/bibm/2022/6819/0",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/10/09445658",
"title": "A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT",
"doi": null,
"abstractUrl": "/journal/tp/2022/10/09445658/1uaajNYaeQw",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNAR1b0Y",
"title": "2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)",
"acronym": "ictai",
"groupId": "1000763",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBp52zt",
"doi": "10.1109/ICTAI.2016.0082",
"title": "Mongolian Named Entity Recognition with Bidirectional Recurrent Neural Networks",
"normalizedTitle": "Mongolian Named Entity Recognition with Bidirectional Recurrent Neural Networks",
"abstract": "Traditional approaches to Named Entity Recognition almost heavily rely on feature engineering. In this paper, we introduce a kind of bidirectional recurrent neural network with long short memory (BLSTM) to capture bidirectional and long dependencies in a sentence without any feature set. Our model combines BLSTM network with Conditional Random Field (CRF) layer to jointly decode the best output. Additionally, this model inputs the concatenation of Mongolian morpheme and character representation. Experimental results show that the bidirectional recurrent neural networks significantly outperform traditional CRF model using manual features.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Traditional approaches to Named Entity Recognition almost heavily rely on feature engineering. In this paper, we introduce a kind of bidirectional recurrent neural network with long short memory (BLSTM) to capture bidirectional and long dependencies in a sentence without any feature set. Our model combines BLSTM network with Conditional Random Field (CRF) layer to jointly decode the best output. Additionally, this model inputs the concatenation of Mongolian morpheme and character representation. Experimental results show that the bidirectional recurrent neural networks significantly outperform traditional CRF model using manual features.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Traditional approaches to Named Entity Recognition almost heavily rely on feature engineering. In this paper, we introduce a kind of bidirectional recurrent neural network with long short memory (BLSTM) to capture bidirectional and long dependencies in a sentence without any feature set. Our model combines BLSTM network with Conditional Random Field (CRF) layer to jointly decode the best output. Additionally, this model inputs the concatenation of Mongolian morpheme and character representation. Experimental results show that the bidirectional recurrent neural networks significantly outperform traditional CRF model using manual features.",
"fno": "4459a495",
"keywords": [
"Context",
"Recurrent Neural Networks",
"Natural Language Processing",
"Labeling",
"Training",
"Logic Gates",
"Vocabulary",
"BLSTM CRF",
"Named Entity Recognition",
"Mongolian Morpheme Representation",
"Recurrent Neural Networks"
],
"authors": [
{
"affiliation": null,
"fullName": "Weihua Wang",
"givenName": "Weihua",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Feilong Bao",
"givenName": "Feilong",
"surname": "Bao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Guanglai Gao",
"givenName": "Guanglai",
"surname": "Gao",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ictai",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-11-01T00:00:00",
"pubType": "proceedings",
"pages": "495-500",
"year": "2016",
"issn": "2375-0197",
"isbn": "978-1-5090-4459-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4459a490",
"articleId": "12OmNzZEACn",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4459a501",
"articleId": "12OmNC943BZ",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/dsc/2018/4210/0/421001a685",
"title": "Multimodal Object Classification Using Bidirectional Gated Recurrent Unit Networks",
"doi": null,
"abstractUrl": "/proceedings-article/dsc/2018/421001a685/12OmNBTawgq",
"parentPublication": {
"id": "proceedings/dsc/2018/4210/0",
"title": "2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dexa/2017/1051/0/1051a118",
"title": "Opinion Expression Detection via Deep Bidirectional C-GRUs",
"doi": null,
"abstractUrl": "/proceedings-article/dexa/2017/1051a118/12OmNs59JOC",
"parentPublication": {
"id": "proceedings/dexa/2017/1051/0",
"title": "2017 28th International Workshop on Database and Expert Systems Applications (DEXA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2007/03/n0441",
"title": "Bidirectional Long Short-Term Memory Networks for Predicting the Subcellular Localization of Eukaryotic Proteins",
"doi": null,
"abstractUrl": "/journal/tb/2007/03/n0441/13rRUygT7wL",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acie/2022/7973/0/797300a067",
"title": "An Intelligent Named Entity Recognition Method Based on IoT Professional Knowledge",
"doi": null,
"abstractUrl": "/proceedings-article/acie/2022/797300a067/1Fiyb0oNzEc",
"parentPublication": {
"id": "proceedings/acie/2022/7973/0",
"title": "2022 2nd Asia Conference on Information Engineering (ACIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ithings-greencom-cpscom-smartdata/2019/2980/0/298000a649",
"title": "Named Entity Recognition in Chinese Electronic Medical Record Using Attention Mechanism",
"doi": null,
"abstractUrl": "/proceedings-article/ithings-greencom-cpscom-smartdata/2019/298000a649/1ehBE7SrRkI",
"parentPublication": {
"id": "proceedings/ithings-greencom-cpscom-smartdata/2019/2980/0",
"title": "2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icis/2019/0801/0/08940185",
"title": "Entity Relationship Extraction Based on BLSTM Model",
"doi": null,
"abstractUrl": "/proceedings-article/icis/2019/08940185/1gjRKwRQEUw",
"parentPublication": {
"id": "proceedings/icis/2019/0801/0",
"title": "2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2021/04/09039695",
"title": "Neural Named Entity Boundary Detection",
"doi": null,
"abstractUrl": "/journal/tk/2021/04/09039695/1igS3qngNQQ",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isctt/2020/8575/0/857500a529",
"title": "Ship Trajectory Prediction Based on Attention in Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/isctt/2020/857500a529/1rHeNS5VdMA",
"parentPublication": {
"id": "proceedings/isctt/2020/8575/0",
"title": "2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09378151",
"title": "BuTTER: BidirecTional LSTM for Food Named-Entity Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09378151/1s64Y7qMs5q",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377803",
"title": "Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377803/1s64mZ5Lexi",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNBTawvw",
"title": "2017 28th International Workshop on Database and Expert Systems Applications (DEXA)",
"acronym": "dexa",
"groupId": "1000180",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNs59JOC",
"doi": "10.1109/DEXA.2017.40",
"title": "Opinion Expression Detection via Deep Bidirectional C-GRUs",
"normalizedTitle": "Opinion Expression Detection via Deep Bidirectional C-GRUs",
"abstract": "The ability to accurately detect opinion expression in a document is an essential and fundamental task in opinion mining. In this work, we consider opinion expression detection as a sequence labeling task. We describe deep neural network frameworks that consist of convolutional neural networks (CNNs) and bidirectional gated units (Bi-GRUs). CNNs are capable of capturing local features in a sequence, while Bi-GRUs, a type of recurrent neural network (RNN) variant, are able to extract features from sequence data. The properties of these two networks provide the framework to effectively detect opinion expression. Experimental results show that our methods significantly outperform traditional methods like conditional random field (CRF) and previous state-of-the-art deep RNN methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The ability to accurately detect opinion expression in a document is an essential and fundamental task in opinion mining. In this work, we consider opinion expression detection as a sequence labeling task. We describe deep neural network frameworks that consist of convolutional neural networks (CNNs) and bidirectional gated units (Bi-GRUs). CNNs are capable of capturing local features in a sequence, while Bi-GRUs, a type of recurrent neural network (RNN) variant, are able to extract features from sequence data. The properties of these two networks provide the framework to effectively detect opinion expression. Experimental results show that our methods significantly outperform traditional methods like conditional random field (CRF) and previous state-of-the-art deep RNN methods.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The ability to accurately detect opinion expression in a document is an essential and fundamental task in opinion mining. In this work, we consider opinion expression detection as a sequence labeling task. We describe deep neural network frameworks that consist of convolutional neural networks (CNNs) and bidirectional gated units (Bi-GRUs). CNNs are capable of capturing local features in a sequence, while Bi-GRUs, a type of recurrent neural network (RNN) variant, are able to extract features from sequence data. The properties of these two networks provide the framework to effectively detect opinion expression. Experimental results show that our methods significantly outperform traditional methods like conditional random field (CRF) and previous state-of-the-art deep RNN methods.",
"fno": "1051a118",
"keywords": [
"Feature Extraction",
"Labeling",
"Recurrent Neural Networks",
"Convolution",
"Natural Language Processing",
"Opinion Mining",
"Opinion Expression Detection",
"Sequence Labeling",
"Recurrent Neural Network",
"Convolution Neural Network"
],
"authors": [
{
"affiliation": null,
"fullName": "Xiaoxia Xie",
"givenName": "Xiaoxia",
"surname": "Xie",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "dexa",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-08-01T00:00:00",
"pubType": "proceedings",
"pages": "118-122",
"year": "2017",
"issn": "2378-3915",
"isbn": "978-1-5386-1051-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "1051a113",
"articleId": "12OmNBuL1a8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "1051a123",
"articleId": "12OmNyRg4Fb",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icdar/2017/3586/1/3586a902",
"title": "A GRU-Based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2017/3586a902/12OmNAMbZHe",
"parentPublication": {
"id": "proceedings/icdar/2017/3586/1",
"title": "2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2016/4459/0/4459a495",
"title": "Mongolian Named Entity Recognition with Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2016/4459a495/12OmNBp52zt",
"parentPublication": {
"id": "proceedings/ictai/2016/4459/0",
"title": "2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2009/4800/0/05349599",
"title": "Theme detection an exploration of opinion subjectivity",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2009/05349599/12OmNyLiuAT",
"parentPublication": {
"id": "proceedings/acii/2009/4800/0",
"title": "2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/synasc/2014/8447/0/07034688",
"title": "Opinion Influence Analysis in Online Forum Threads",
"doi": null,
"abstractUrl": "/proceedings-article/synasc/2014/07034688/12OmNyrZLDW",
"parentPublication": {
"id": "proceedings/synasc/2014/8447/0",
"title": "2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isuc/2008/3433/0/3433a447",
"title": "Experiments on Personal Opinion Expression and Consensus Building using ",
"doi": null,
"abstractUrl": "/proceedings-article/isuc/2008/3433a447/12OmNzdoMvT",
"parentPublication": {
"id": "proceedings/isuc/2008/3433/0",
"title": "2008 Second International Symposium on Universal Communication",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2013/02/mex2013020015",
"title": "New Avenues in Opinion Mining and Sentiment Analysis",
"doi": null,
"abstractUrl": "/magazine/ex/2013/02/mex2013020015/13rRUyft7z8",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbd/2018/8034/0/803400a311",
"title": "A Hybrid BLSTM-C Neural Network Proposed for Chinese Text Classification",
"doi": null,
"abstractUrl": "/proceedings-article/cbd/2018/803400a311/17D45WKWnK1",
"parentPublication": {
"id": "proceedings/cbd/2018/8034/0",
"title": "2018 Sixth International Conference on Advanced Cloud and Big Data (CBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ica/2022/6936/0/693600a018",
"title": "Prototyping Agents for Resolving Opinion Biases Toward Facilitating Sublation of Conflict in Web-based Discussions",
"doi": null,
"abstractUrl": "/proceedings-article/ica/2022/693600a018/1JvaIWStvlS",
"parentPublication": {
"id": "proceedings/ica/2022/6936/0",
"title": "2022 IEEE International Conference on Agents (ICA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2022/01/08922646",
"title": "On-the-Fly Facial Expression Prediction Using LSTM Encoded Appearance-Suppressed Dynamics",
"doi": null,
"abstractUrl": "/journal/ta/2022/01/08922646/1fvZawN5SpO",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2019/0858/0/09006119",
"title": "End-to-End Joint Opinion Role Labeling with BERT",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2019/09006119/1hJszvIe6vS",
"parentPublication": {
"id": "proceedings/big-data/2019/0858/0",
"title": "2019 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNCbU3aO",
"title": "2013 12th International Conference on Document Analysis and Recognition (ICDAR)",
"acronym": "icdar",
"groupId": "1000219",
"volume": "0",
"displayVolume": "0",
"year": "2013",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzC5T1i",
"doi": "10.1109/ICDAR.2013.212",
"title": "Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks",
"normalizedTitle": "Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks",
"abstract": "Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. We evaluated BLSTM networks for two cases: one ignoring the character's shape variations and the second is considering them. The recognition error rate at character level for first case is 5.15% and for the second is 13.6%. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shape-matching based method is also presented.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. We evaluated BLSTM networks for two cases: one ignoring the character's shape variations and the second is considering them. The recognition error rate at character level for first case is 5.15% and for the second is 13.6%. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shape-matching based method is also presented.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. We evaluated BLSTM networks for two cases: one ignoring the character's shape variations and the second is considering them. The recognition error rate at character level for first case is 5.15% and for the second is 13.6%. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shape-matching based method is also presented.",
"fno": "06628777",
"keywords": [
"Shape",
"Training",
"Recurrent Neural Networks",
"Optical Character Recognition Software",
"Feature Extraction",
"Handwriting Recognition",
"Error Analysis",
"RNN",
"Urdu OCR",
"BLSTM Networks"
],
"authors": [
{
"affiliation": null,
"fullName": "Adnan Ul-Hasan",
"givenName": "Adnan",
"surname": "Ul-Hasan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Saad Bin Ahmed",
"givenName": "Saad Bin",
"surname": "Ahmed",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Faisal Rashid",
"givenName": "Faisal",
"surname": "Rashid",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Faisal Shafait",
"givenName": "Faisal",
"surname": "Shafait",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Thomas M. Breuel",
"givenName": "Thomas M.",
"surname": "Breuel",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icdar",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2013-08-01T00:00:00",
"pubType": "proceedings",
"pages": "1061-1065",
"year": "2013",
"issn": "1520-5363",
"isbn": "978-0-7695-4999-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06628776",
"articleId": "12OmNAS9zED",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06628778",
"articleId": "12OmNwtEEKS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/viz/2009/3734/0/3734a191",
"title": "Segmentation of Printed Urdu Scripts Using Structural Features",
"doi": null,
"abstractUrl": "/proceedings-article/viz/2009/3734a191/12OmNAqU4VV",
"parentPublication": {
"id": "proceedings/viz/2009/3734/0",
"title": "Visualisation, International Conference in",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2017/3586/1/3586a131",
"title": "Impact of Ligature Coverage on Training Practical Urdu OCR Systems",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2017/3586a131/12OmNCctfdA",
"parentPublication": {
"id": "proceedings/icdar/2017/3586/1",
"title": "2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icct/2017/3030/0/08324060",
"title": "Urdu ligature recognition techniques-A review",
"doi": null,
"abstractUrl": "/proceedings-article/icct/2017/08324060/12OmNvCRglB",
"parentPublication": {
"id": "proceedings/icct/2017/3030/0",
"title": "2017 International Conference on Intelligent Communication and Computational Techniques (ICCT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2003/1960/2/196021183",
"title": "Recognition of Printed Urdu Script",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2003/196021183/12OmNxHrymw",
"parentPublication": {
"id": "proceedings/icdar/2003/1960/2",
"title": "Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icfhr/2012/2262/0/06424415",
"title": "Word Spotting Based Retrieval of Urdu Handwritten Documents",
"doi": null,
"abstractUrl": "/proceedings-article/icfhr/2012/06424415/12OmNxjjEfd",
"parentPublication": {
"id": "proceedings/icfhr/2012/2262/0",
"title": "2012 International Conference on Frontiers in Handwriting Recognition (ICFHR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fit/2015/9666/0/9666a334",
"title": "Printed Urdu Nastalique Script Recognition Using Analytical Approach",
"doi": null,
"abstractUrl": "/proceedings-article/fit/2015/9666a334/12OmNz4SOvr",
"parentPublication": {
"id": "proceedings/fit/2015/9666/0",
"title": "2015 13th International Conference on Frontiers of Information Technology (FIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fit/2017/3567/0/356701a087",
"title": "Ligature Analysis-based Urdu OCR Framework",
"doi": null,
"abstractUrl": "/proceedings-article/fit/2017/356701a087/12OmNzFdt4q",
"parentPublication": {
"id": "proceedings/fit/2017/3567/0",
"title": "2017 International Conference on Frontiers of Information Technology (FIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2013/4999/0/06628705",
"title": "High-Performance OCR for Printed English and Fraktur Using LSTM Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2013/06628705/12OmNzXWZDj",
"parentPublication": {
"id": "proceedings/icdar/2013/4999/0",
"title": "2013 12th International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acpr/2017/3354/0/3354a747",
"title": "Unconstrained OCR for Urdu Using Deep CNN-RNN Hybrid Networks",
"doi": null,
"abstractUrl": "/proceedings-article/acpr/2017/3354a747/17D45WHONjV",
"parentPublication": {
"id": "proceedings/acpr/2017/3354/0",
"title": "2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icfhr/2020/9966/0/996600a169",
"title": "An attention based method for offline handwritten Urdu text recognition",
"doi": null,
"abstractUrl": "/proceedings-article/icfhr/2020/996600a169/1p2Vw0Vyh32",
"parentPublication": {
"id": "proceedings/icfhr/2020/9966/0",
"title": "2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1F1VTu4w3Kg",
"title": "2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"acronym": "ipdps",
"groupId": "1000530",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1F1W7FYKuHK",
"doi": "10.1109/IPDPS53621.2022.00096",
"title": "Task-based Acceleration of Bidirectional Recurrent Neural Networks on Multi-core Architectures",
"normalizedTitle": "Task-based Acceleration of Bidirectional Recurrent Neural Networks on Multi-core Architectures",
"abstract": "This paper proposes a novel parallel execution model for Bidirectional Recurrent Neural Networks (BRNNs), B-Par (Bidirectional-Parallelization), which exploits data and control dependencies for forward and reverse input computations. B-Par divides BRNN workloads across different parallel tasks by defining input and output dependencies for each RNN cell in both forward and reverse orders. B-Par does not require per-layer barriers to synchronize the parallel execution of BRNNs. We evaluate B-Par considering the TIDIGITS speech database and the Wikipedia data-set. Our experiments indicate that B-Par outperforms the state-of-the-art deep learning frameworks TensorFlow-Keras and Pytorch by achieving up to 2.34× and 9.16× speed-ups, respectively, on modern multi-core CPU architectures while preserving accuracy. Moreover, we analyze in detail aspects like task granularity, locality, or parallel efficiency to illustrate the benefits of B-Par.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper proposes a novel parallel execution model for Bidirectional Recurrent Neural Networks (BRNNs), B-Par (Bidirectional-Parallelization), which exploits data and control dependencies for forward and reverse input computations. B-Par divides BRNN workloads across different parallel tasks by defining input and output dependencies for each RNN cell in both forward and reverse orders. B-Par does not require per-layer barriers to synchronize the parallel execution of BRNNs. We evaluate B-Par considering the TIDIGITS speech database and the Wikipedia data-set. Our experiments indicate that B-Par outperforms the state-of-the-art deep learning frameworks TensorFlow-Keras and Pytorch by achieving up to 2.34× and 9.16× speed-ups, respectively, on modern multi-core CPU architectures while preserving accuracy. Moreover, we analyze in detail aspects like task granularity, locality, or parallel efficiency to illustrate the benefits of B-Par.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper proposes a novel parallel execution model for Bidirectional Recurrent Neural Networks (BRNNs), B-Par (Bidirectional-Parallelization), which exploits data and control dependencies for forward and reverse input computations. B-Par divides BRNN workloads across different parallel tasks by defining input and output dependencies for each RNN cell in both forward and reverse orders. B-Par does not require per-layer barriers to synchronize the parallel execution of BRNNs. We evaluate B-Par considering the TIDIGITS speech database and the Wikipedia data-set. Our experiments indicate that B-Par outperforms the state-of-the-art deep learning frameworks TensorFlow-Keras and Pytorch by achieving up to 2.34× and 9.16× speed-ups, respectively, on modern multi-core CPU architectures while preserving accuracy. Moreover, we analyze in detail aspects like task granularity, locality, or parallel efficiency to illustrate the benefits of B-Par.",
"fno": "810600a941",
"keywords": [
"Deep Learning Artificial Intelligence",
"Multiprocessing Systems",
"Parallel Processing",
"Recurrent Neural Nets",
"Wikipedia Dataset",
"Multicore CPU Architecture",
"Task Granularity",
"Task Based Acceleration",
"Bidirectional Recurrent Neural Networks",
"Multicore Architectures",
"Parallel Execution Model",
"Bidirectional Parallelization",
"TIDIGITS Speech Database",
"BRNN Workload",
"Tensor Flow Keras Framework",
"Pytorch Framework",
"Deep Learning",
"Training",
"Recurrent Neural Networks",
"Scalability",
"Computer Architecture",
"Parallel Processing",
"Transformers",
"Deep Neural Network DNN",
"Bidirectional Re Current Neural Networks BRN Ns",
"Long Short Term Memory LSTM",
"Gated Recurrent Units GRU",
"Task Parallelism"
],
"authors": [
{
"affiliation": "Barcelona Supercomputing Center (BSC), Universitat Politècnica de Catalunya (UPC),Computer Science Department",
"fullName": "Robin Kumar Sharma",
"givenName": "Robin Kumar",
"surname": "Sharma",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Barcelona Supercomputing Center (BSC), Universitat Politècnica de Catalunya (UPC),Computer Science Department",
"fullName": "Marc Casas",
"givenName": "Marc",
"surname": "Casas",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ipdps",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-05-01T00:00:00",
"pubType": "proceedings",
"pages": "941-951",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-8106-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "810600a930",
"articleId": "1F1VWHheAlq",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "810600a952",
"articleId": "1F1W5GWup7W",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/dsc/2018/4210/0/421001a685",
"title": "Multimodal Object Classification Using Bidirectional Gated Recurrent Unit Networks",
"doi": null,
"abstractUrl": "/proceedings-article/dsc/2018/421001a685/12OmNBTawgq",
"parentPublication": {
"id": "proceedings/dsc/2018/4210/0",
"title": "2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2016/4459/0/4459a495",
"title": "Mongolian Named Entity Recognition with Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2016/4459a495/12OmNBp52zt",
"parentPublication": {
"id": "proceedings/ictai/2016/4459/0",
"title": "2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipps/1994/5602/0/0288294",
"title": "Parallel bidirectional heuristic search with dynamic process re-direction",
"doi": null,
"abstractUrl": "/proceedings-article/ipps/1994/0288294/12OmNCdk2VU",
"parentPublication": {
"id": "proceedings/ipps/1994/5602/0",
"title": "Parallel Processing Symposium, International",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754239",
"title": "Bidirectional Hybrid LSTM Based Recurrent Neural Network for Multi-view Stereo",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754239/1CpcE7ttZNC",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600q6505",
"title": "L-Verse: Bidirectional Generation Between Image and Text",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600q6505/1H1mIPeYLrq",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/micro/2022/6272/0/627200a744",
"title": "Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation",
"doi": null,
"abstractUrl": "/proceedings-article/micro/2022/627200a744/1HMSw1zbJaE",
"parentPublication": {
"id": "proceedings/micro/2022/6272/0",
"title": "2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fpl/2022/7390/0/739000a218",
"title": "FSHMEM: Supporting Partitioned Global Address Space on FPGAs for Large-Scale Hardware Acceleration Infrastructure",
"doi": null,
"abstractUrl": "/proceedings-article/fpl/2022/739000a218/1KJwEZVDkB2",
"parentPublication": {
"id": "proceedings/fpl/2022/7390/0",
"title": "2022 32nd International Conference on Field-Programmable Logic and Applications (FPL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313452",
"title": "Cross2Self-attentive Bidirectional Recurrent Neural Network with BERT for Biomedical Semantic Text Similarity",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313452/1qmg91OKLXG",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377803",
"title": "Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377803/1s64mZ5Lexi",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mascots/2021/5838/0/09614292",
"title": "Enabling Extremely Fine-grained Parallelism via Scalable Concurrent Queues on Modern Many-core Architectures",
"doi": null,
"abstractUrl": "/proceedings-article/mascots/2021/09614292/1yJZ1uVKjLO",
"parentPublication": {
"id": "proceedings/mascots/2021/5838/0",
"title": "2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1lgop3uO3QI",
"title": "2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)",
"acronym": "compsac",
"groupId": "1000143",
"volume": "2",
"displayVolume": "2",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1cYiotBhVoA",
"doi": "10.1109/COMPSAC.2019.10204",
"title": "Modeling Genome Data Using Bidirectional LSTM",
"normalizedTitle": "Modeling Genome Data Using Bidirectional LSTM",
"abstract": "Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence alignment. In particular, we use the deep bidirectional LSTM to learn features of LSH. The obtained LSH is then can be utilized to perform sequence alignment. We demonstrate the feasibility of the modeling sequences using the proposed LSTM-based model by aligning the short read queries over the reference genome. We use the human reference genome as our training dataset, in addition to a set of short reads generated using Illumina sequencing technology. The ultimate goal is to align query sequences into a reference genome. We first decompose the reference genome into multiple sequences. These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors. These vectors are what we call the trained LSH, which can then be used for sequence alignment. The case study shows that using the introduced LSTM-based model, we achieve higher accuracy with the number of epochs.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence alignment. In particular, we use the deep bidirectional LSTM to learn features of LSH. The obtained LSH is then can be utilized to perform sequence alignment. We demonstrate the feasibility of the modeling sequences using the proposed LSTM-based model by aligning the short read queries over the reference genome. We use the human reference genome as our training dataset, in addition to a set of short reads generated using Illumina sequencing technology. The ultimate goal is to align query sequences into a reference genome. We first decompose the reference genome into multiple sequences. These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors. These vectors are what we call the trained LSH, which can then be used for sequence alignment. The case study shows that using the introduced LSTM-based model, we achieve higher accuracy with the number of epochs.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence alignment. In particular, we use the deep bidirectional LSTM to learn features of LSH. The obtained LSH is then can be utilized to perform sequence alignment. We demonstrate the feasibility of the modeling sequences using the proposed LSTM-based model by aligning the short read queries over the reference genome. We use the human reference genome as our training dataset, in addition to a set of short reads generated using Illumina sequencing technology. The ultimate goal is to align query sequences into a reference genome. We first decompose the reference genome into multiple sequences. These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors. These vectors are what we call the trained LSH, which can then be used for sequence alignment. The case study shows that using the introduced LSTM-based model, we achieve higher accuracy with the number of epochs.",
"fno": "260702a183",
"keywords": [
"Bioinformatics",
"Genetics",
"Genomics",
"Learning Artificial Intelligence",
"Neural Net Architecture",
"Query Processing",
"Recurrent Neural Nets",
"Vectors",
"Human Reference Genome",
"Illumina Sequencing Technology",
"Long Range Dependencies",
"Deep Bidirectional LSTM",
"Sequence Modeling",
"Locality Sensitive Hashing Based Sequence Alignment",
"Query Sequences",
"LSH",
"LSTM Based Model",
"Bidirectional Long Short Term Memory",
"Recurrent Neural Network Architecture",
"Genome Data Modeling",
"Short Read Queries",
"Fixed Length Vectors",
"Logic Gates",
"Genomics",
"Bioinformatics",
"Recurrent Neural Networks",
"Computational Modeling",
"Tools",
"Sequential Analysis",
"Bidirectional Long Short Term Memory LSTM Sequence Alignment Locality Sensitive Hashing LSH"
],
"authors": [
{
"affiliation": "Georgia Institute of Technology",
"fullName": "Neda Tavakoli",
"givenName": "Neda",
"surname": "Tavakoli",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "compsac",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-07-01T00:00:00",
"pubType": "proceedings",
"pages": "183-188",
"year": "2019",
"issn": "0730-3157",
"isbn": "978-1-7281-2607-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "260702a177",
"articleId": "1cYisBpQvlu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "260702a189",
"articleId": "1cYiok1t5hC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ldav/2014/5215/0/07013210",
"title": "Bacterial gene neighborhood investigation environment: A large-scale genome visualization for big displays",
"doi": null,
"abstractUrl": "/proceedings-article/ldav/2014/07013210/12OmNAmVH5w",
"parentPublication": {
"id": "proceedings/ldav/2014/5215/0",
"title": "2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2017/1324/0/132401a119",
"title": "Predictive Genome Analysis Using Partial DNA Sequencing Data",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2017/132401a119/12OmNs5rkTh",
"parentPublication": {
"id": "proceedings/bibe/2017/1324/0",
"title": "2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2015/9926/0/07364056",
"title": "Enabling graph appliance for genome assembly",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2015/07364056/12OmNwbcJ55",
"parentPublication": {
"id": "proceedings/big-data/2015/9926/0",
"title": "2015 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse-c/2017/1589/0/07965285",
"title": "App Genome: Callback Sequencing in Android",
"doi": null,
"abstractUrl": "/proceedings-article/icse-c/2017/07965285/12OmNwekjES",
"parentPublication": {
"id": "proceedings/icse-c/2017/1589/0",
"title": "2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cisis/2013/4992/0/06603952",
"title": "Mining Polymorphic SSRs from Individual Genome Sequences",
"doi": null,
"abstractUrl": "/proceedings-article/cisis/2013/06603952/12OmNyfdOYk",
"parentPublication": {
"id": "proceedings/cisis/2013/4992/0",
"title": "2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2018/5520/0/552000b216",
"title": "Scalable De Novo Genome Assembly Using Pregel",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2018/552000b216/14Fq0UEfx1Y",
"parentPublication": {
"id": "proceedings/icde/2018/5520/0",
"title": "2018 IEEE 34th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/5555/01/09925084",
"title": "Bidirectional Association Discovery Leads to Precise Identification of Lung Cancer Biomarkers and Genome Taxa Class",
"doi": null,
"abstractUrl": "/journal/tb/5555/01/09925084/1HBHTI4EpMY",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2021/02/08731736",
"title": "Scalable De Novo Genome Assembly Using a Pregel-Like Graph-Parallel System",
"doi": null,
"abstractUrl": "/journal/tb/2021/02/08731736/1aCbtw87Jdu",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/mi/2020/05/09154510",
"title": "Accelerating Genome Analysis: A Primer on an Ongoing Journey",
"doi": null,
"abstractUrl": "/magazine/mi/2020/05/09154510/1lZzYVaH7lC",
"parentPublication": {
"id": "mags/mi",
"title": "IEEE Micro",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2021/06/09528958",
"title": "GapPredict – A Language Model for Resolving Gaps in Draft Genome Assemblies",
"doi": null,
"abstractUrl": "/journal/tb/2021/06/09528958/1wB2ppnazOo",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1gyr6w5YIIU",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1gyrpNNO9QA",
"doi": "10.1109/CVPR.2019.00567",
"title": "Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference",
"normalizedTitle": "Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference",
"abstract": "Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at https://github.com/YoYo000/MVSNet.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at https://github.com/YoYo000/MVSNet.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at https://github.com/YoYo000/MVSNet.",
"fno": "329300f520",
"keywords": [
"Image Reconstruction",
"Image Resolution",
"Learning Artificial Intelligence",
"Recurrent Neural Nets",
"Stereo Image Processing",
"Deep Learning",
"Memory Consuming Cost Volume Regularization",
"Recurrent Neural Network",
"3 D Cost Volume",
"R MVS Net",
"Gated Recurrent Unit",
"Memory Consumption",
"High Resolution Reconstruction",
"Recurrent MVS Net",
"High Resolution Multiview Stereo Depth Inference",
"Recurrent Multiview Stereo Network",
"3 D From Multiview And Sensors"
],
"authors": [
{
"affiliation": "The Hong Kong Univ. of Science and Technology",
"fullName": "Yao Yao",
"givenName": "Yao",
"surname": "Yao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "HKUST",
"fullName": "Zixin Luo",
"givenName": "Zixin",
"surname": "Luo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "HKUST",
"fullName": "Shiwei Li",
"givenName": "Shiwei",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "HKUST",
"fullName": "Tianwei Shen",
"givenName": "Tianwei",
"surname": "Shen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Altizure",
"fullName": "Tian Fang",
"givenName": "Tian",
"surname": "Fang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Hong Kong Univ. of Science and Technology",
"fullName": "Long Quan",
"givenName": "Long",
"surname": "Quan",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-06-01T00:00:00",
"pubType": "proceedings",
"pages": "5520-5529",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-3293-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "329300f510",
"articleId": "1gyrFtcgR3i",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "329300f530",
"articleId": "1gys1ciPoys",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2012/2216/0/06460252",
"title": "Depth-map merging for Multi-View Stereo with high resolution images",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460252/12OmNwNwzMv",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f712",
"title": "EPP-MVSNet: Epipolar-assembling based Depth Prediction for Multi-view Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f712/1BmG7clsJj2",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200g158",
"title": "Just a Few Points are All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g158/1BmGFNhUk5a",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754239",
"title": "Bidirectional Hybrid LSTM Based Recurrent Neural Network for Multi-view Stereo",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754239/1CpcE7ttZNC",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300k0451",
"title": "P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300k0451/1hVlB5gaWSQ",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800e876",
"title": "Cost Volume Pyramid Based Depth Inference for Multi-View Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800e876/1m3nW3zQ07u",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800b946",
"title": "Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800b946/1m3o4lY7hsc",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2020/8128/0/812800a394",
"title": "BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2020/812800a394/1qyxkbFekXC",
"parentPublication": {
"id": "proceedings/3dv/2020/8128/0",
"title": "2020 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2021/3864/0/09428281",
"title": "High-Resolution Multi-View Stereo with Dynamic Depth Edge Flow",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2021/09428281/1uilFvw0hR6",
"parentPublication": {
"id": "proceedings/icme/2021/3864/0",
"title": "2021 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900h522",
"title": "Self-supervised Learning of Depth Inference for Multi-view Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900h522/1yeHXy8lpYY",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1qmfHK8AjMQ",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1qmg91OKLXG",
"doi": "10.1109/BIBM49941.2020.9313452",
"title": "Cross2Self-attentive Bidirectional Recurrent Neural Network with BERT for Biomedical Semantic Text Similarity",
"normalizedTitle": "Cross2Self-attentive Bidirectional Recurrent Neural Network with BERT for Biomedical Semantic Text Similarity",
"abstract": "Estimating the similarity of biomedical sentence pair is an important component in such natural language processing (NLP) tasks as text retrieval and text summarization with great amount of biomedical information growing. Deep learning-based approaches have been successfully applied to the task, but they often rely on traditional pre-trained context-independent word embedding. Bidirectional Encoder Representations from Transformers (BERT) is recently employed to pre-train contextualized word/sentence representation models via bidirectional Transformers, outperforming the state-of-the-art for many NLP tasks. The mutual semantic influence between sentences is important for estimating semantic textual similarity, which is neglected in existing methods including BERT. On the other hand, biomedical corpora mainly consist of syntactic complex and long sentences. Owing to the above-mentioned issues, we proposed a hybrid architecture, integrating the pre-trained BERT and downstream bidirectional recurrent neural network (bi-RNN). The proposed model enhanced the sentence semantic representation via employing the self-attention instead of global attention to perform cross attention between sentences. Meanwhile, bi-RNN reduced redundant information in the output of BERT. Experimental results show that the best fine-tuned models consistently outperform previous methods and advance the state-of-the-art for clinical semantic textual similarity in OHNLP 2018 task 2, with up to 0.6% increase in Pearson correlation coefficient.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Estimating the similarity of biomedical sentence pair is an important component in such natural language processing (NLP) tasks as text retrieval and text summarization with great amount of biomedical information growing. Deep learning-based approaches have been successfully applied to the task, but they often rely on traditional pre-trained context-independent word embedding. Bidirectional Encoder Representations from Transformers (BERT) is recently employed to pre-train contextualized word/sentence representation models via bidirectional Transformers, outperforming the state-of-the-art for many NLP tasks. The mutual semantic influence between sentences is important for estimating semantic textual similarity, which is neglected in existing methods including BERT. On the other hand, biomedical corpora mainly consist of syntactic complex and long sentences. Owing to the above-mentioned issues, we proposed a hybrid architecture, integrating the pre-trained BERT and downstream bidirectional recurrent neural network (bi-RNN). The proposed model enhanced the sentence semantic representation via employing the self-attention instead of global attention to perform cross attention between sentences. Meanwhile, bi-RNN reduced redundant information in the output of BERT. Experimental results show that the best fine-tuned models consistently outperform previous methods and advance the state-of-the-art for clinical semantic textual similarity in OHNLP 2018 task 2, with up to 0.6% increase in Pearson correlation coefficient.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Estimating the similarity of biomedical sentence pair is an important component in such natural language processing (NLP) tasks as text retrieval and text summarization with great amount of biomedical information growing. Deep learning-based approaches have been successfully applied to the task, but they often rely on traditional pre-trained context-independent word embedding. Bidirectional Encoder Representations from Transformers (BERT) is recently employed to pre-train contextualized word/sentence representation models via bidirectional Transformers, outperforming the state-of-the-art for many NLP tasks. The mutual semantic influence between sentences is important for estimating semantic textual similarity, which is neglected in existing methods including BERT. On the other hand, biomedical corpora mainly consist of syntactic complex and long sentences. Owing to the above-mentioned issues, we proposed a hybrid architecture, integrating the pre-trained BERT and downstream bidirectional recurrent neural network (bi-RNN). The proposed model enhanced the sentence semantic representation via employing the self-attention instead of global attention to perform cross attention between sentences. Meanwhile, bi-RNN reduced redundant information in the output of BERT. Experimental results show that the best fine-tuned models consistently outperform previous methods and advance the state-of-the-art for clinical semantic textual similarity in OHNLP 2018 task 2, with up to 0.6% increase in Pearson correlation coefficient.",
"fno": "09313452",
"keywords": [
"Deep Learning Artificial Intelligence",
"Information Retrieval",
"Medical Information Systems",
"Natural Language Processing",
"Recurrent Neural Nets",
"Text Analysis",
"Word Processing",
"Bidirectional Encoder Representations From Transformers",
"NLP Tasks",
"Mutual Semantic Influence",
"Long Sentences",
"Pre Trained BERT",
"Downstream Bidirectional Recurrent Neural Network",
"Sentence Semantic Representation",
"Redundant Information",
"Clinical Semantic Textual Similarity",
"Cross 2 Self Attentive Bidirectional Recurrent Neural Network",
"Biomedical Semantic Text Similarity",
"Biomedical Sentence Pair",
"Natural Language Processing Tasks",
"Text Retrieval",
"Text Summarization",
"Biomedical Information",
"Deep Learning Based Approaches",
"Pre Trained Context Independent Word Embedding",
"Bit Error Rate",
"Semantics",
"Biological System Modeling",
"Task Analysis",
"Recurrent Neural Networks",
"Lenses",
"Computer Science",
"Cross Attention",
"BERT",
"Semantic Textual Similarity",
"Mutual Semantic Influence"
],
"authors": [
{
"affiliation": "Dalian University Of Technology,College of Computer Science and Technology,Dalian,Liaoning,China",
"fullName": "Zhengguang Li",
"givenName": "Zhengguang",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dalian University Of Technology,College of Computer Science and Technology,Dalian,Liaoning,China",
"fullName": "Hongfei Lin",
"givenName": "Hongfei",
"surname": "Lin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dalian University Of Technology,College of Computer Science and Technology,Dalian,Liaoning,China",
"fullName": "Chen Shen",
"givenName": "Chen",
"surname": "Shen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Software Institute Dalian Jiaotong University,Dalian,Liaoning,China",
"fullName": "Wei Zheng",
"givenName": "Wei",
"surname": "Zheng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dalian University Of Technology,College of Computer Science and Technology,Dalian,Liaoning,China",
"fullName": "Zhihao Yang",
"givenName": "Zhihao",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dalian University Of Technology,College of Computer Science and Technology,Dalian,Liaoning,China",
"fullName": "Jian Wang",
"givenName": "Jian",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-12-01T00:00:00",
"pubType": "proceedings",
"pages": "1051-1054",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-6215-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09313330",
"articleId": "1qmfMF0COt2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09313601",
"articleId": "1qmfYQ5BZZu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2021/0126/0/09669855",
"title": "C2BERT: Cross-contrast BERT for Chinese Biomedical Sentence Representation",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669855/1A9VRlgss1i",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669869",
"title": "TL-BERT: A Novel Biomedical Relation Extraction Approach",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669869/1A9WkaPDGrm",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669841",
"title": "On the application of BERT models for nanopore methylation detection",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669841/1A9Wo26lWj6",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cecit/2021/3757/0/375700b199",
"title": "Text hashing by semantic information based on BERT model",
"doi": null,
"abstractUrl": "/proceedings-article/cecit/2021/375700b199/1CdEU1JCbOU",
"parentPublication": {
"id": "proceedings/cecit/2021/3757/0",
"title": "2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ccat/2022/9069/0/906900a060",
"title": "Research on Assistant Diagnostic Method of TCM Based on BERT and BiGRU Recurrent Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/ccat/2022/906900a060/1JZ3SI2xqFi",
"parentPublication": {
"id": "proceedings/ccat/2022/9069/0",
"title": "2022 International Conference on Computer Applications Technology (CCAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313160",
"title": "Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313160/1qmgafEcTmg",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigcomp/2021/8924/0/892400a091",
"title": "Cluster-aware Semantic Vector Learning using BERT in Natural Language Understanding",
"doi": null,
"abstractUrl": "/proceedings-article/bigcomp/2021/892400a091/1rRccroKNTW",
"parentPublication": {
"id": "proceedings/bigcomp/2021/8924/0",
"title": "2021 IEEE International Conference on Big Data and Smart Computing (BigComp)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmcce/2020/2314/0/231400b625",
"title": "Chinese Event Detection Combining BERT Model with Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icmcce/2020/231400b625/1tzyCmu101G",
"parentPublication": {
"id": "proceedings/icmcce/2020/2314/0",
"title": "2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2021/0132/0/013200a507",
"title": "Contrastive Representations Pre-Training for Enhanced Discharge Summary BERT",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2021/013200a507/1xIOO9qqC9a",
"parentPublication": {
"id": "proceedings/ichi/2021/0132/0",
"title": "2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2021/0132/0/013200a503",
"title": "Self-supervised extractive text summarization for biomedical literatures",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2021/013200a503/1xIOOzMhIQw",
"parentPublication": {
"id": "proceedings/ichi/2021/0132/0",
"title": "2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1s645BaTzVu",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"acronym": "big-data",
"groupId": "1802964",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1s64mZ5Lexi",
"doi": "10.1109/BigData50022.2020.9377803",
"title": "Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks",
"normalizedTitle": "Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks",
"abstract": "Software vulnerability detection in source code is a fundamental problem in cyber-security. Aiming at discovering the vulnerability automatically, this paper proposes an open source software vulnerability detection method based on attention-based bidirectional recurrent neural networks. Based on the high-level and generalizable function representations that obtained from the abstract syntax tree(AST), an attention-based bidirectional recurrent neural networks is devised to capture the sequential and important code elements in vulnerability detection from the large number of features that the deep learning model has learned. Experimental results confirm that the huge potential of the proposed new vulnerability detection method which is not only more effective than Convolutional Neural Networks(CNN) but also better than traditional Bidirectional Recurrent Neural Networks(BRNN) in reducing the false negative rate at the price of increasing the false positive rate.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Software vulnerability detection in source code is a fundamental problem in cyber-security. Aiming at discovering the vulnerability automatically, this paper proposes an open source software vulnerability detection method based on attention-based bidirectional recurrent neural networks. Based on the high-level and generalizable function representations that obtained from the abstract syntax tree(AST), an attention-based bidirectional recurrent neural networks is devised to capture the sequential and important code elements in vulnerability detection from the large number of features that the deep learning model has learned. Experimental results confirm that the huge potential of the proposed new vulnerability detection method which is not only more effective than Convolutional Neural Networks(CNN) but also better than traditional Bidirectional Recurrent Neural Networks(BRNN) in reducing the false negative rate at the price of increasing the false positive rate.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Software vulnerability detection in source code is a fundamental problem in cyber-security. Aiming at discovering the vulnerability automatically, this paper proposes an open source software vulnerability detection method based on attention-based bidirectional recurrent neural networks. Based on the high-level and generalizable function representations that obtained from the abstract syntax tree(AST), an attention-based bidirectional recurrent neural networks is devised to capture the sequential and important code elements in vulnerability detection from the large number of features that the deep learning model has learned. Experimental results confirm that the huge potential of the proposed new vulnerability detection method which is not only more effective than Convolutional Neural Networks(CNN) but also better than traditional Bidirectional Recurrent Neural Networks(BRNN) in reducing the false negative rate at the price of increasing the false positive rate.",
"fno": "09377803",
"keywords": [
"Convolutional Neural Nets",
"Deep Learning Artificial Intelligence",
"Explanation",
"Public Domain Software",
"Recurrent Neural Nets",
"Security Of Data",
"Source Code Software",
"Tree Data Structures",
"Explainable Software Vulnerability Detection",
"Attention Based Bidirectional Recurrent Neural Networks",
"Open Source Software Vulnerability Detection",
"Cybersecurity",
"Abstract Syntax Tree",
"AST",
"Deep Learning",
"Convolutional Neural Networks",
"CNN",
"Deep Learning",
"Recurrent Neural Networks",
"Big Data",
"Syntactics",
"Feature Extraction",
"Data Models",
"Open Source Software",
"Software Vulnerability Detection",
"Deep Learning",
"Machine Learning",
"Attention Mechanism"
],
"authors": [
{
"affiliation": "Nanjing University of Posts and Telecommunications (NJUPT),School of Computer Science and Technology,Nanjing,China",
"fullName": "Yi Mao",
"givenName": "Yi",
"surname": "Mao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University of Posts and Telecommunications (NJUPT),School of Computer Science and Technology,Nanjing,China",
"fullName": "Yun Li",
"givenName": "Yun",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University of Posts and Telecommunications (NJUPT),School of Computer Science and Technology,Nanjing,China",
"fullName": "Jiatai Sun",
"givenName": "Jiatai",
"surname": "Sun",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Washington University in St.Louis,Department of Computer Science and Engineering,St.Louis,USA",
"fullName": "Yixin Chen",
"givenName": "Yixin",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "big-data",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-12-01T00:00:00",
"pubType": "proceedings",
"pages": "4651-4656",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-6251-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09378254",
"articleId": "1s64eQumliw",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09378044",
"articleId": "1s64zE6RrBC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/dsc/2018/4210/0/421001a685",
"title": "Multimodal Object Classification Using Bidirectional Gated Recurrent Unit Networks",
"doi": null,
"abstractUrl": "/proceedings-article/dsc/2018/421001a685/12OmNBTawgq",
"parentPublication": {
"id": "proceedings/dsc/2018/4210/0",
"title": "2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2016/4459/0/4459a495",
"title": "Mongolian Named Entity Recognition with Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2016/4459a495/12OmNBp52zt",
"parentPublication": {
"id": "proceedings/ictai/2016/4459/0",
"title": "2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2004/2128/4/212840593",
"title": "Improvement of Bidirectional Recurrent Neural Network for Learning Long-Term Dependencies",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2004/212840593/12OmNxETald",
"parentPublication": {
"id": "proceedings/icpr/2004/2128/0",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2018/04/07919264",
"title": "Video Super-Resolution via Bidirectional Recurrent Convolutional Networks",
"doi": null,
"abstractUrl": "/journal/tp/2018/04/07919264/13rRUxAAT8X",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754239",
"title": "Bidirectional Hybrid LSTM Based Recurrent Neural Network for Multi-view Stereo",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754239/1CpcE7ttZNC",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdps/2022/8106/0/810600a941",
"title": "Task-based Acceleration of Bidirectional Recurrent Neural Networks on Multi-core Architectures",
"doi": null,
"abstractUrl": "/proceedings-article/ipdps/2022/810600a941/1F1W7FYKuHK",
"parentPublication": {
"id": "proceedings/ipdps/2022/8106/0",
"title": "2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbctis/2022/9691/0/969100a056",
"title": "Reentrancy Vulnerability Detection of Smart Contract Based on Bidirectional Sequential Neural Network with Hierarchical Attention Mechanism",
"doi": null,
"abstractUrl": "/proceedings-article/icbctis/2022/969100a056/1FRL7IWAFWg",
"parentPublication": {
"id": "proceedings/icbctis/2022/9691/0",
"title": "2022 International Conference on Blockchain Technology and Information Security (ICBCTIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isctt/2020/8575/0/857500a529",
"title": "Ship Trajectory Prediction Based on Attention in Bidirectional Recurrent Neural Networks",
"doi": null,
"abstractUrl": "/proceedings-article/isctt/2020/857500a529/1rHeNS5VdMA",
"parentPublication": {
"id": "proceedings/isctt/2020/8575/0",
"title": "2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse-companion/2021/1219/0/121900a304",
"title": "Vulnerability Detection is Just the Beginning",
"doi": null,
"abstractUrl": "/proceedings-article/icse-companion/2021/121900a304/1sET7HK8t4k",
"parentPublication": {
"id": "proceedings/icse-companion/2021/1219/0/",
"title": "2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2021/3574/0/357400a892",
"title": "Self-Attention based Automated Vulnerability Detection with Effective Data Representation",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2021/357400a892/1zxKXr5CPE4",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2021/3574/0",
"title": "2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNy2agRT",
"title": "2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW)",
"acronym": "compsacw",
"groupId": "1800173",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvSbBGm",
"doi": "10.1109/COMPSACW.2014.25",
"title": "Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection",
"normalizedTitle": "Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection",
"abstract": "Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal models. Second, the classifiers are trained using very large datasets which may be time consuming to obtain in practice. When the size of training dataset is small, the performance of a single BN classifier is significantly reduced due to its inability to represent the whole probability distribution. To alleviate these problems, we build a Bayesian classifier by Bayesian Model Averaging(BMA) over the k-best BN classifiers, called Bayesian Network Model Averaging (BNMA) classifier. We train and evaluate BNMA classifier on the NSL-KDD dataset, which is less redundant, thus more judicial than the commonly used KDD Cup 99 dataset. We show that the BNMA classifier performs significantly better in terms of detection accuracy than the Naive Bayes classifier and the BN classifier built with heuristic method. We also show that the BNMA classifier trained using a smaller dataset outperforms two other classifiers trained using a larger dataset. This also implies that the BNMA is beneficial in accelerating the detection process due to its less dependance on the potentially prolonged process of collecting large training datasets.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal models. Second, the classifiers are trained using very large datasets which may be time consuming to obtain in practice. When the size of training dataset is small, the performance of a single BN classifier is significantly reduced due to its inability to represent the whole probability distribution. To alleviate these problems, we build a Bayesian classifier by Bayesian Model Averaging(BMA) over the k-best BN classifiers, called Bayesian Network Model Averaging (BNMA) classifier. We train and evaluate BNMA classifier on the NSL-KDD dataset, which is less redundant, thus more judicial than the commonly used KDD Cup 99 dataset. We show that the BNMA classifier performs significantly better in terms of detection accuracy than the Naive Bayes classifier and the BN classifier built with heuristic method. We also show that the BNMA classifier trained using a smaller dataset outperforms two other classifiers trained using a larger dataset. This also implies that the BNMA is beneficial in accelerating the detection process due to its less dependance on the potentially prolonged process of collecting large training datasets.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal models. Second, the classifiers are trained using very large datasets which may be time consuming to obtain in practice. When the size of training dataset is small, the performance of a single BN classifier is significantly reduced due to its inability to represent the whole probability distribution. To alleviate these problems, we build a Bayesian classifier by Bayesian Model Averaging(BMA) over the k-best BN classifiers, called Bayesian Network Model Averaging (BNMA) classifier. We train and evaluate BNMA classifier on the NSL-KDD dataset, which is less redundant, thus more judicial than the commonly used KDD Cup 99 dataset. We show that the BNMA classifier performs significantly better in terms of detection accuracy than the Naive Bayes classifier and the BN classifier built with heuristic method. We also show that the BNMA classifier trained using a smaller dataset outperforms two other classifiers trained using a larger dataset. This also implies that the BNMA is beneficial in accelerating the detection process due to its less dependance on the potentially prolonged process of collecting large training datasets.",
"fno": "3578a128",
"keywords": [
"Training",
"Bayes Methods",
"Accuracy",
"Intrusion Detection",
"Computational Modeling",
"Probability Distribution",
"Testing",
"Detection Accuracy",
"Intrusion Detection System",
"Bayesian Network",
"Bayesian Model Averaging"
],
"authors": [
{
"affiliation": null,
"fullName": "Liyuan Xiao",
"givenName": "Liyuan",
"surname": "Xiao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yetian Chen",
"givenName": "Yetian",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Carl K. Chang",
"givenName": "Carl K.",
"surname": "Chang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "compsacw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-07-01T00:00:00",
"pubType": "proceedings",
"pages": "128-133",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-3578-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "3578a122",
"articleId": "12OmNCcKQPH",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "3578a134",
"articleId": "12OmNAS9zxs",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccea/2010/3982/2/3982b325",
"title": "Comparing Single and Multiple Bayesian Classifiers Approaches for Network Intrusion Detection",
"doi": null,
"abstractUrl": "/proceedings-article/iccea/2010/3982b325/12OmNAXxX5f",
"parentPublication": {
"id": "proceedings/iccea/2010/3982/2",
"title": "Computer Engineering and Applications, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ams/2008/3136/0/3136a463",
"title": "A Probabilistic Approach for Network Intrusion Detection",
"doi": null,
"abstractUrl": "/proceedings-article/ams/2008/3136a463/12OmNBa2iG2",
"parentPublication": {
"id": "proceedings/ams/2008/3136/0",
"title": "Asia International Conference on Modelling & Simulation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nas/2013/5034/0/5034a307",
"title": "An Effective Feature Selection Approach for Network Intrusion Detection",
"doi": null,
"abstractUrl": "/proceedings-article/nas/2013/5034a307/12OmNvSKNKc",
"parentPublication": {
"id": "proceedings/nas/2013/5034/0",
"title": "2013 IEEE 8th International Conference on Networking, Architecture, and Storage (NAS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acsat/2013/2758/0/2758a005",
"title": "Modified Full Bayesian Networks Classifiers for Medical Diagnosis",
"doi": null,
"abstractUrl": "/proceedings-article/acsat/2013/2758a005/12OmNwNwzGU",
"parentPublication": {
"id": "proceedings/acsat/2013/2758/0",
"title": "2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iciap/2007/2877/0/28770073",
"title": "Using Bayesian Network for combining classifiers",
"doi": null,
"abstractUrl": "/proceedings-article/iciap/2007/28770073/12OmNx6xHni",
"parentPublication": {
"id": "proceedings/iciap/2007/2877/0",
"title": "2007 14th International Conference on Image Analysis and Processing - ICIAP 2007",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2012/0227/1/06495080",
"title": "Revising the Outputs of a Decision Tree with Expert Knowledge: Application to Intrusion Detection and Alert Correlation",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2012/06495080/12OmNxdDFQA",
"parentPublication": {
"id": "proceedings/ictai/2012/0227/1",
"title": "2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2012/0227/1/06495035",
"title": "Using Relative Classification Probability to Increase Accuracy of Restricted Structure Bayesian Network Classifiers",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2012/06495035/12OmNzuIjof",
"parentPublication": {
"id": "proceedings/ictai/2012/0227/1",
"title": "2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/11/07364252",
"title": "Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data",
"doi": null,
"abstractUrl": "/journal/tp/2016/11/07364252/13rRUB6Sq1H",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2013/02/tts2013020237",
"title": "Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers",
"doi": null,
"abstractUrl": "/journal/ts/2013/02/tts2013020237/13rRUxYIMX0",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bracis/2018/8023/0/802300a432",
"title": "Bayesian Classifiers Supported by Ranking for Decision Making in Robot Soccer",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2018/802300a432/17D45WIXbP0",
"parentPublication": {
"id": "proceedings/bracis/2018/8023/0",
"title": "2018 7th Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNynsbDd",
"title": "2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT)",
"acronym": "acsat",
"groupId": "1802667",
"volume": "0",
"displayVolume": "0",
"year": "2013",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNwNwzGU",
"doi": "10.1109/ACSAT.2013.10",
"title": "Modified Full Bayesian Networks Classifiers for Medical Diagnosis",
"normalizedTitle": "Modified Full Bayesian Networks Classifiers for Medical Diagnosis",
"abstract": "This paper is present a modified Bayesian Network (BN) for Full Bayesian Classifier (FBC) for used it to diagnoses the condition of a patient from some of symptoms and medical history. The conditions are the heart diseases and the nervous diseases. Modified for FBC is depended on common structure known as naïve Bayes. Determining the network structure is D-separated by the variable. Each variable has CPT and each disease (table) has Probability. By modified the equation for find CPT for each variable and Probability for each disease (table) in Modified-FBC (M-FBC) structure, the system for diagnosis is designed. The experimental resulted show that the successful ratio of heart diseases (93%) and nervous system diseases (98%) approximately.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper is present a modified Bayesian Network (BN) for Full Bayesian Classifier (FBC) for used it to diagnoses the condition of a patient from some of symptoms and medical history. The conditions are the heart diseases and the nervous diseases. Modified for FBC is depended on common structure known as naïve Bayes. Determining the network structure is D-separated by the variable. Each variable has CPT and each disease (table) has Probability. By modified the equation for find CPT for each variable and Probability for each disease (table) in Modified-FBC (M-FBC) structure, the system for diagnosis is designed. The experimental resulted show that the successful ratio of heart diseases (93%) and nervous system diseases (98%) approximately.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper is present a modified Bayesian Network (BN) for Full Bayesian Classifier (FBC) for used it to diagnoses the condition of a patient from some of symptoms and medical history. The conditions are the heart diseases and the nervous diseases. Modified for FBC is depended on common structure known as naïve Bayes. Determining the network structure is D-separated by the variable. Each variable has CPT and each disease (table) has Probability. By modified the equation for find CPT for each variable and Probability for each disease (table) in Modified-FBC (M-FBC) structure, the system for diagnosis is designed. The experimental resulted show that the successful ratio of heart diseases (93%) and nervous system diseases (98%) approximately.",
"fno": "2758a005",
"keywords": [
"Diseases",
"Bayes Methods",
"Mutual Information",
"Equations",
"Heart",
"Medical Diagnostic Imaging",
"Training Data",
"Naive Bayes",
"Bayesian Network BN",
"Full Bayesian Classifier FBC",
"Modified FBC M FBC",
"CPT",
"Probability"
],
"authors": [
{
"affiliation": null,
"fullName": "Ahmed T. Sadiq AlObaidi",
"givenName": "Ahmed T. Sadiq",
"surname": "AlObaidi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Noor Thamer Mahmood",
"givenName": "Noor Thamer",
"surname": "Mahmood",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "acsat",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2013-12-01T00:00:00",
"pubType": "proceedings",
"pages": "5-12",
"year": "2013",
"issn": null,
"isbn": "978-1-4799-2758-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "2758a001",
"articleId": "12OmNB836PF",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "2758a013",
"articleId": "12OmNx76TSu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/acsat/2015/0423/0/07478725",
"title": "Cuckoo Search-Based Bayesian Networks for Medical Estimation System",
"doi": null,
"abstractUrl": "/proceedings-article/acsat/2015/07478725/12OmNAu1Fnw",
"parentPublication": {
"id": "proceedings/acsat/2015/0423/0",
"title": "2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/5209a220",
"title": "A Multi-task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209a220/12OmNCcKQDL",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2015/8493/0/8493a170",
"title": "Identifying Medical Terms Related to Specific Diseases",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2015/8493a170/12OmNqAU6os",
"parentPublication": {
"id": "proceedings/icdmw/2015/8493/0",
"title": "2015 IEEE International Conference on Data Mining Workshop (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icccnt/2013/3926/0/06726740",
"title": "Heart diseases diagnosis using neural network",
"doi": null,
"abstractUrl": "/proceedings-article/icccnt/2013/06726740/12OmNx9nGGe",
"parentPublication": {
"id": "proceedings/icccnt/2013/3926/0",
"title": "2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdpsw/2014/4116/0/4116b631",
"title": "Parallel Bayesian Network Modelling for Pervasive Health Monitoring System",
"doi": null,
"abstractUrl": "/proceedings-article/ipdpsw/2014/4116b631/12OmNxQOjFu",
"parentPublication": {
"id": "proceedings/ipdpsw/2014/4116/0",
"title": "2014 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/itme/2015/8302/0/8302a243",
"title": "Statistical Analysis of 2115 Hospitalization Death Cases",
"doi": null,
"abstractUrl": "/proceedings-article/itme/2015/8302a243/12OmNyRPgJW",
"parentPublication": {
"id": "proceedings/itme/2015/8302/0",
"title": "2015 7th International Conference on Information Technology in Medicine and Education (ITME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2013/5089/0/5089a175",
"title": "Risk Prediction of a Multiple Sclerosis Diagnosis",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2013/5089a175/12OmNywfKw4",
"parentPublication": {
"id": "proceedings/ichi/2013/5089/0",
"title": "2013 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/alpit/2008/3273/0/3273a220",
"title": "A Proposal of Heart Diseases Diagnosis Method Using Analysis of Face Color",
"doi": null,
"abstractUrl": "/proceedings-article/alpit/2008/3273a220/12OmNzaQoiJ",
"parentPublication": {
"id": "proceedings/alpit/2008/3273/0",
"title": "Advanced Language Processing and Web Information Technology, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2019/7474/0/747400b928",
"title": "A Prescription Trend Analysis using Medical Insurance Claim Big Data",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2019/747400b928/1aDSVKHgfxm",
"parentPublication": {
"id": "proceedings/icde/2019/7474/0",
"title": "2019 IEEE 35th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csci/2019/5584/0/558400a910",
"title": "Diagnosing Heart Disease Types from Chest X-Rays Using a Deep Learning Approach",
"doi": null,
"abstractUrl": "/proceedings-article/csci/2019/558400a910/1jdE09LwR1u",
"parentPublication": {
"id": "proceedings/csci/2019/5584/0",
"title": "2019 International Conference on Computational Science and Computational Intelligence (CSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNyO8tMK",
"title": "2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"acronym": "issrew",
"groupId": "1002972",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzDvShe",
"doi": "10.1109/ISSREW.2017.60",
"title": "Establishing Verification and Validation Objectives for Safety-Critical Bayesian Networks",
"normalizedTitle": "Establishing Verification and Validation Objectives for Safety-Critical Bayesian Networks",
"abstract": "The assurance of autonomous systems and the technologies that drive them is a major research challenge in the safety-critical systems engineering domain. The nature of many of these Machine Learning (ML) and Artificial Intelligence (AI) approaches raises a number of additional, technology-specific assurance concerns. One such approach is the Bayesian Network (BN) probabilistic modelling framework. Bayesian Networks and the family of modelling techniques they belong to form the basis of many AI applications. However, little research has been conducted into the assurance of BN-based systems for use in safety-critical applications. This paper explores some of the key distinctions between BN-based software-intensive systems and conventional software systems. It introduces a modelling framework that explicitly captures BN-based systemspecific considerations and facilitates both the communication of assurance concerns between safety practitioners and system stakeholders, and the subsequent safety analysis of the system itself. It demonstrates how this approach can be used to develop specific verification and validation objectives for a BN-based system in a medical application.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The assurance of autonomous systems and the technologies that drive them is a major research challenge in the safety-critical systems engineering domain. The nature of many of these Machine Learning (ML) and Artificial Intelligence (AI) approaches raises a number of additional, technology-specific assurance concerns. One such approach is the Bayesian Network (BN) probabilistic modelling framework. Bayesian Networks and the family of modelling techniques they belong to form the basis of many AI applications. However, little research has been conducted into the assurance of BN-based systems for use in safety-critical applications. This paper explores some of the key distinctions between BN-based software-intensive systems and conventional software systems. It introduces a modelling framework that explicitly captures BN-based systemspecific considerations and facilitates both the communication of assurance concerns between safety practitioners and system stakeholders, and the subsequent safety analysis of the system itself. It demonstrates how this approach can be used to develop specific verification and validation objectives for a BN-based system in a medical application.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The assurance of autonomous systems and the technologies that drive them is a major research challenge in the safety-critical systems engineering domain. The nature of many of these Machine Learning (ML) and Artificial Intelligence (AI) approaches raises a number of additional, technology-specific assurance concerns. One such approach is the Bayesian Network (BN) probabilistic modelling framework. Bayesian Networks and the family of modelling techniques they belong to form the basis of many AI applications. However, little research has been conducted into the assurance of BN-based systems for use in safety-critical applications. This paper explores some of the key distinctions between BN-based software-intensive systems and conventional software systems. It introduces a modelling framework that explicitly captures BN-based systemspecific considerations and facilitates both the communication of assurance concerns between safety practitioners and system stakeholders, and the subsequent safety analysis of the system itself. It demonstrates how this approach can be used to develop specific verification and validation objectives for a BN-based system in a medical application.",
"fno": "2387a302",
"keywords": [
"Bayes Methods",
"Belief Networks",
"Formal Specification",
"Formal Verification",
"Learning Artificial Intelligence",
"Medical Computing",
"Safety Critical Bayesian Networks",
"Safety Critical Systems Engineering Domain",
"AI Applications",
"Verification Objective",
"Bayesian Network Probabilistic Modelling Framework",
"BN Based Software Intensive Systems",
"Medical Application",
"Validation Objectives",
"Safety",
"Computational Modeling",
"Adaptation Models",
"Software",
"Bayes Methods",
"Data Models",
"Bayesian Networks",
"Autonomous Systems",
"Machine Learning",
"Safety Critical",
"Mission Critical",
"Assurance",
"Reference Model"
],
"authors": [
{
"affiliation": null,
"fullName": "Mark Douthwaite",
"givenName": "Mark",
"surname": "Douthwaite",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Tim Kelly",
"givenName": "Tim",
"surname": "Kelly",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "issrew",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-10-01T00:00:00",
"pubType": "proceedings",
"pages": "302-309",
"year": "2017",
"issn": null,
"isbn": "978-1-5386-2387-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "2387a294",
"articleId": "12OmNwCJONW",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "2387a310",
"articleId": "12OmNzYwc6p",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/re/2010/8022/0/05636545",
"title": "Creating Safety Requirements Traceability for Assuring and Recertifying Legacy Safety-Critical Systems",
"doi": null,
"abstractUrl": "/proceedings-article/re/2010/05636545/12OmNAXPymc",
"parentPublication": {
"id": "proceedings/re/2010/8022/0",
"title": "2010 18th IEEE International Requirements Engineering Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wosocer/2011/4617/0/4617a001",
"title": "Challenges for an Open and Evolutionary Approach to Safety Assurance and Certification of Safety-Critical Systems",
"doi": null,
"abstractUrl": "/proceedings-article/wosocer/2011/4617a001/12OmNCfSqU8",
"parentPublication": {
"id": "proceedings/wosocer/2011/4617/0",
"title": "Software Certification, International Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/issrew/2015/1944/0/07392061",
"title": "Automated compositional safety analysis using component fault trees",
"doi": null,
"abstractUrl": "/proceedings-article/issrew/2015/07392061/12OmNwDj1hf",
"parentPublication": {
"id": "proceedings/issrew/2015/1944/0",
"title": "2015 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/co/2008/12/04712513",
"title": "Software Tools for Safety-Critical Systems According to DO-254",
"doi": null,
"abstractUrl": "/magazine/co/2008/12/04712513/13rRUxjyWZ7",
"parentPublication": {
"id": "mags/co",
"title": "Computer",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iciibms/2018/7516/3/08550000",
"title": "Research on Risk Management of Construction Safety based on Bayesian Network",
"doi": null,
"abstractUrl": "/proceedings-article/iciibms/2018/08550000/17D45WrVg4K",
"parentPublication": {
"id": "proceedings/iciibms/2018/7516/3",
"title": "2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/issrew/2018/9443/0/944300a329",
"title": "Toward a Systematic and Safety Evidence Productive Verification Approach for Safety-Critical Systems",
"doi": null,
"abstractUrl": "/proceedings-article/issrew/2018/944300a329/17D45XvMcaO",
"parentPublication": {
"id": "proceedings/issrew/2018/9443/0",
"title": "2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/issrew/2022/7679/0/767900a394",
"title": "Assurance Guidance for Machine Learning in a Safety-Critical System",
"doi": null,
"abstractUrl": "/proceedings-article/issrew/2022/767900a394/1JqDOQhbZMQ",
"parentPublication": {
"id": "proceedings/issrew/2022/7679/0",
"title": "2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/issrew/2022/7679/0/767900a326",
"title": "Assuring Safety-Critical Machine Learning Enabled Systems: Challenges and Promise",
"doi": null,
"abstractUrl": "/proceedings-article/issrew/2022/767900a326/1JqDTgpa41O",
"parentPublication": {
"id": "proceedings/issrew/2022/7679/0",
"title": "2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/so/2021/03/09108244",
"title": "Visualizing Change in Agile Safety-Critical Systems",
"doi": null,
"abstractUrl": "/magazine/so/2021/03/09108244/1koLfWp1M76",
"parentPublication": {
"id": "mags/so",
"title": "IEEE Software",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2020/5382/0/09374327",
"title": "Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2020/09374327/1rUJ1pjOWIM",
"parentPublication": {
"id": "proceedings/ichi/2020/5382/0",
"title": "2020 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNy5hRdd",
"title": "2014 IEEE International Conference on Data Mining (ICDM)",
"acronym": "icdm",
"groupId": "1000179",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNzzxuxd",
"doi": "10.1109/ICDM.2014.126",
"title": "Learning Sparse Gaussian Bayesian Network Structure by Variable Grouping",
"normalizedTitle": "Learning Sparse Gaussian Bayesian Network Structure by Variable Grouping",
"abstract": "Bayesian networks (BNs) are popular for modeling conditional distributions of variables and causal relationships, especially in biological settings such as protein interactions, gene regulatory networks and microbial interactions. Previous BN structure learning algorithms treat variables with similar tendency separately. In this paper, we propose a grouped sparse Gaussian BN (GSGBN) structure learning algorithm which creates BN based on three assumptions: (i) variables follow a multivariate Gaussian distribution, (ii) the network only contains a few edges (sparse), (iii) similar variables have less-divergent sets of parents, while not-so-similar ones should have divergent sets of parents (variable grouping). We use L1 regularization to make the learned network sparse, and another term to incorporate shared information among variables. For similar variables, GSGBN tends to penalize the differences of similar variables' parent sets more, compared to those not-so-similar variables' parent sets. The similarity of variables is learned from the data by alternating optimization, without prior domain knowledge. Based on this new definition of the optimal BN, a coordinate descent algorithm and a projected gradient descent algorithm are developed to obtain edges of the network and also similarity of variables. Experimental results on both simulated and real datasets show that GSGBN has substantially superior prediction performance for structure learning when compared to several existing algorithms.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bayesian networks (BNs) are popular for modeling conditional distributions of variables and causal relationships, especially in biological settings such as protein interactions, gene regulatory networks and microbial interactions. Previous BN structure learning algorithms treat variables with similar tendency separately. In this paper, we propose a grouped sparse Gaussian BN (GSGBN) structure learning algorithm which creates BN based on three assumptions: (i) variables follow a multivariate Gaussian distribution, (ii) the network only contains a few edges (sparse), (iii) similar variables have less-divergent sets of parents, while not-so-similar ones should have divergent sets of parents (variable grouping). We use L1 regularization to make the learned network sparse, and another term to incorporate shared information among variables. For similar variables, GSGBN tends to penalize the differences of similar variables' parent sets more, compared to those not-so-similar variables' parent sets. The similarity of variables is learned from the data by alternating optimization, without prior domain knowledge. Based on this new definition of the optimal BN, a coordinate descent algorithm and a projected gradient descent algorithm are developed to obtain edges of the network and also similarity of variables. Experimental results on both simulated and real datasets show that GSGBN has substantially superior prediction performance for structure learning when compared to several existing algorithms.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Bayesian networks (BNs) are popular for modeling conditional distributions of variables and causal relationships, especially in biological settings such as protein interactions, gene regulatory networks and microbial interactions. Previous BN structure learning algorithms treat variables with similar tendency separately. In this paper, we propose a grouped sparse Gaussian BN (GSGBN) structure learning algorithm which creates BN based on three assumptions: (i) variables follow a multivariate Gaussian distribution, (ii) the network only contains a few edges (sparse), (iii) similar variables have less-divergent sets of parents, while not-so-similar ones should have divergent sets of parents (variable grouping). We use L1 regularization to make the learned network sparse, and another term to incorporate shared information among variables. For similar variables, GSGBN tends to penalize the differences of similar variables' parent sets more, compared to those not-so-similar variables' parent sets. The similarity of variables is learned from the data by alternating optimization, without prior domain knowledge. Based on this new definition of the optimal BN, a coordinate descent algorithm and a projected gradient descent algorithm are developed to obtain edges of the network and also similarity of variables. Experimental results on both simulated and real datasets show that GSGBN has substantially superior prediction performance for structure learning when compared to several existing algorithms.",
"fno": "4302b073",
"keywords": [
"Gaussian Distribution",
"Probability Distribution",
"Benchmark Testing",
"Optimization",
"Sensitivity",
"Linear Regression",
"Bismuth",
"Microbial Interactions",
"Bayesian Network",
"Sparsity",
"Variable Grouping"
],
"authors": [
{
"affiliation": null,
"fullName": "Jie Yang",
"givenName": "Jie",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Henry C.M. Leung",
"givenName": "Henry C.M.",
"surname": "Leung",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "S.M. Yiu",
"givenName": "S.M.",
"surname": "Yiu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yunpeng Cai",
"givenName": "Yunpeng",
"surname": "Cai",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Francis Y.L. Chin",
"givenName": "Francis Y.L.",
"surname": "Chin",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icdm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-12-01T00:00:00",
"pubType": "proceedings",
"pages": "1073-1078",
"year": "2014",
"issn": "1550-4786",
"isbn": "978-1-4799-4302-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4302b067",
"articleId": "12OmNvAAtKJ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4302b079",
"articleId": "12OmNCxbXGQ",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icwe/2008/3261/0/3261a203",
"title": "The Use of Bayesian Networks for Web Effort Estimation: Further Investigation",
"doi": null,
"abstractUrl": "/proceedings-article/icwe/2008/3261a203/12OmNAlvHZz",
"parentPublication": {
"id": "proceedings/icwe/2008/3261/0",
"title": "Web Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2016/5473/0/07837932",
"title": "Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2016/07837932/12OmNB8Cj2v",
"parentPublication": {
"id": "proceedings/icdm/2016/5473/0",
"title": "2016 IEEE 16th International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2013/4999/0/06628828",
"title": "Bayesian Network Structure Learning and Inference Methods for Handwriting",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2013/06628828/12OmNvlg8jx",
"parentPublication": {
"id": "proceedings/icdar/2013/4999/0",
"title": "2013 12th International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/prni/2015/7145/0/7145a025",
"title": "Multivariate Effect Ranking via Adaptive Sparse PLS",
"doi": null,
"abstractUrl": "/proceedings-article/prni/2015/7145a025/12OmNxAlA22",
"parentPublication": {
"id": "proceedings/prni/2015/7145/0",
"title": "2015 International Workshop on Pattern Recognition in NeuroImaging (PRNI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2009/3895/0/3895a387",
"title": "Stacked Gaussian Process Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2009/3895a387/12OmNxecS4p",
"parentPublication": {
"id": "proceedings/icdm/2009/3895/0",
"title": "2009 Ninth IEEE International Conference on Data Mining",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2010/4256/0/4256a791",
"title": "Block-GP: Scalable Gaussian Process Regression for Multimodal Data",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2010/4256a791/12OmNzxyiNf",
"parentPublication": {
"id": "proceedings/icdm/2010/4256/0",
"title": "2010 IEEE International Conference on Data Mining",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/11/07364252",
"title": "Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data",
"doi": null,
"abstractUrl": "/journal/tp/2016/11/07364252/13rRUB6Sq1H",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2013/06/ttp2013061328",
"title": "A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data",
"doi": null,
"abstractUrl": "/journal/tp/2013/06/ttp2013061328/13rRUwInvKD",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/1998/11/i1133",
"title": "Bayesian Approaches to Gaussian Mixture Modeling",
"doi": null,
"abstractUrl": "/journal/tp/1998/11/i1133/13rRUwwJWGG",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/apsec/2018/1970/0/197000a504",
"title": "Kurtosis and Skewness Adjustment for Software Effort Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/apsec/2018/197000a504/1b66qEyOxuE",
"parentPublication": {
"id": "proceedings/apsec/2018/1970/0",
"title": "2018 25th Asia-Pacific Software Engineering Conference (APSEC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1bhJu4ahcC4",
"title": "2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)",
"acronym": "synasc",
"groupId": "1001577",
"volume": "0",
"displayVolume": "0",
"year": "2018",
"__typename": "ProceedingType"
},
"article": {
"id": "1bhJxTrNy1O",
"doi": "10.1109/SYNASC.2018.00017",
"title": "Inferring, Learning and Modelling Complex Systems with Bayesian Networks. A Tutorial",
"normalizedTitle": "Inferring, Learning and Modelling Complex Systems with Bayesian Networks. A Tutorial",
"abstract": "Bayesian networks, BN, are a formalism for probabilistic reasoning that have grown increasingly popular for tasks such as classification in data-mining. In some situations, the structure of the Bayesian network can be given by an expert. If not, retrieving it automatically from a database of cases is a NP-hard problem; notably because of the complexity of the search space. In the last decade, numerous methods have been introduced to learn the networks structure automatically, by simplifying the search space or by using a heuristic in the search space. Most methods deal with completely observed data, but some can deal with incomplete data. In this tutorial we will present, besides BN, other popular classification methods, i.e. Multilayer Perceptrons Network (MLP) and K-nearest neighbor (KNN) an analyze their performance in the context of medical diagnosis.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bayesian networks, BN, are a formalism for probabilistic reasoning that have grown increasingly popular for tasks such as classification in data-mining. In some situations, the structure of the Bayesian network can be given by an expert. If not, retrieving it automatically from a database of cases is a NP-hard problem; notably because of the complexity of the search space. In the last decade, numerous methods have been introduced to learn the networks structure automatically, by simplifying the search space or by using a heuristic in the search space. Most methods deal with completely observed data, but some can deal with incomplete data. In this tutorial we will present, besides BN, other popular classification methods, i.e. Multilayer Perceptrons Network (MLP) and K-nearest neighbor (KNN) an analyze their performance in the context of medical diagnosis.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Bayesian networks, BN, are a formalism for probabilistic reasoning that have grown increasingly popular for tasks such as classification in data-mining. In some situations, the structure of the Bayesian network can be given by an expert. If not, retrieving it automatically from a database of cases is a NP-hard problem; notably because of the complexity of the search space. In the last decade, numerous methods have been introduced to learn the networks structure automatically, by simplifying the search space or by using a heuristic in the search space. Most methods deal with completely observed data, but some can deal with incomplete data. In this tutorial we will present, besides BN, other popular classification methods, i.e. Multilayer Perceptrons Network (MLP) and K-nearest neighbor (KNN) an analyze their performance in the context of medical diagnosis.",
"fno": "062500a027",
"keywords": [
"Belief Networks",
"Computational Complexity",
"Inference Mechanisms",
"Learning Artificial Intelligence",
"Network Theory Graphs",
"Pattern Classification",
"Search Problems",
"Complex Systems",
"Bayesian Network",
"Probabilistic Reasoning",
"NP Hard Problem",
"Search Space",
"Classification Methods",
"Inferring",
"Learning",
"Bayes Methods",
"Inference Algorithms",
"Approximation Algorithms",
"Probabilistic Logic",
"Cognition",
"Probability Distribution",
"Clustering Algorithms",
"Bayesian Networks Structure Learning Software Packages Applications Examples"
],
"authors": [
{
"affiliation": "Institute for Mathematical Statistics and Applied Mathematics, ISMMA Bucharest",
"fullName": "Enachescu Denis",
"givenName": "Enachescu",
"surname": "Denis",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute for Mathematical Statistics and Applied Mathematics, ISMMA Bucharest",
"fullName": "Enachescu Cornelia",
"givenName": "Enachescu",
"surname": "Cornelia",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "synasc",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2018-09-01T00:00:00",
"pubType": "proceedings",
"pages": "27-33",
"year": "2018",
"issn": null,
"isbn": "978-1-7281-0625-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "062500a008",
"articleId": "1bhJvXSZ0aY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "062500a018",
"articleId": "1bhJxrtsrhC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icmssp/2016/4519/0/07946035",
"title": "A Bayesian Network Approach to Study Undergraduates' Brand Consciousness",
"doi": null,
"abstractUrl": "/proceedings-article/icmssp/2016/07946035/12OmNCd2rW3",
"parentPublication": {
"id": "proceedings/icmssp/2016/4519/0",
"title": "2016 International Conference on Multimedia Systems and Signal Processing (ICMSSP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ciis/2017/3886/0/3886a184",
"title": "Discretization of Continuous Variables in Bayesian Networks Based on Matrix Decomposition",
"doi": null,
"abstractUrl": "/proceedings-article/ciis/2017/3886a184/12OmNrYCY0f",
"parentPublication": {
"id": "proceedings/ciis/2017/3886/0",
"title": "2017 International Conference on Computing Intelligence and Information System (CIIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbd/2014/8085/0/8085a039",
"title": "A Score Based Approach towards Improving Bayesian Network Structure Learning",
"doi": null,
"abstractUrl": "/proceedings-article/cbd/2014/8085a039/12OmNvoWV2z",
"parentPublication": {
"id": "proceedings/cbd/2014/8085/0",
"title": "2014 Second International Conference on Advanced Cloud and Big Data (CBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/si/2016/03/07091000",
"title": "Hardware Accelerator for Probabilistic Inference in 65-nm CMOS",
"doi": null,
"abstractUrl": "/journal/si/2016/03/07091000/13rRUyuegmR",
"parentPublication": {
"id": "trans/si",
"title": "IEEE Transactions on Very Large Scale Integration (VLSI) Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaml/2021/2125/0/212500a062",
"title": "Static Analytical Reasoning of Directed Cyclic Graph in the Discrete Case Weight Combination Method",
"doi": null,
"abstractUrl": "/proceedings-article/icaml/2021/212500a062/1B6155T72Ug",
"parentPublication": {
"id": "proceedings/icaml/2021/2125/0",
"title": "2021 3rd International Conference on Applied Machine Learning (ICAML)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpcc-dss-smartcity-dependsys/2022/1993/0/199300a703",
"title": "An Approach of Bayesian Network Learning Based on Optimizing Fringe Search",
"doi": null,
"abstractUrl": "/proceedings-article/hpcc-dss-smartcity-dependsys/2022/199300a703/1LSPaZFd1XG",
"parentPublication": {
"id": "proceedings/hpcc-dss-smartcity-dependsys/2022/1993/0",
"title": "2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bracis/2019/4253/0/425300a687",
"title": "Bayesian Networks for Inference and Discovery of Semantic Relations in a Never-Ending Learning System",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2019/425300a687/1fHkF3NyYWA",
"parentPublication": {
"id": "proceedings/bracis/2019/4253/0",
"title": "2019 8th Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2020/9228/0/922800a572",
"title": "Bayesian Network Structure Learning Using Case-Injected Genetic Algorithms",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2020/922800a572/1pP3vvlwfcI",
"parentPublication": {
"id": "proceedings/ictai/2020/9228/0",
"title": "2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2020/5382/0/09374378",
"title": "Real-time Online Probabilistic Medical Computation using Bayesian Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2020/09374378/1rUJ0pzuXqE",
"parentPublication": {
"id": "proceedings/ichi/2020/5382/0",
"title": "2020 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2020/6406/0/640600b031",
"title": "Probabilistic framework for evaluating the capability and resilience of C4ISR using Bayesian networks",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2020/640600b031/1x3kmy0Ku2Y",
"parentPublication": {
"id": "proceedings/icisce/2020/6406/0",
"title": "2020 7th International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1p1gnrYka5y",
"title": "2020 International Conference on Culture-oriented Science & Technology (ICCST)",
"acronym": "iccst",
"groupId": "1838984",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1p1gr8DDqHS",
"doi": "10.1109/ICCST50977.2020.00100",
"title": "Research on Intelligent Traditional Chinese Medicine Prescription Model Based on Noisy-or Bayesian Network",
"normalizedTitle": "Research on Intelligent Traditional Chinese Medicine Prescription Model Based on Noisy-or Bayesian Network",
"abstract": "Traditional Chinese Medicine (TCM) is the traditional medicine of China, which played an active role in the fight against the pneumonia caused by the novel coronavirus. How to make TCM benefit the general public, and to get the prescriptions of the prestigious Chinese physician without leaving home is a problem worth studying. This is of great significance for the inheritance of TCM. In this paper, an intelligent prescribing model is designed based on the nosiy-or Bayesian network. This model uses the correlation analysis method based on information entropy to obtain the network structure. The entire process greatly reduces the guidance of domain experts. The model uses the medical record data of a prestigious Chinese physician as training data, which can realize intelligent output Chinese medicine prescriptions with input symptom groups. The experimental results show a high accuracy of the model, and it can correctly simulate the diagnosis and treatment process of the prestigious Chinese physician.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Traditional Chinese Medicine (TCM) is the traditional medicine of China, which played an active role in the fight against the pneumonia caused by the novel coronavirus. How to make TCM benefit the general public, and to get the prescriptions of the prestigious Chinese physician without leaving home is a problem worth studying. This is of great significance for the inheritance of TCM. In this paper, an intelligent prescribing model is designed based on the nosiy-or Bayesian network. This model uses the correlation analysis method based on information entropy to obtain the network structure. The entire process greatly reduces the guidance of domain experts. The model uses the medical record data of a prestigious Chinese physician as training data, which can realize intelligent output Chinese medicine prescriptions with input symptom groups. The experimental results show a high accuracy of the model, and it can correctly simulate the diagnosis and treatment process of the prestigious Chinese physician.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Traditional Chinese Medicine (TCM) is the traditional medicine of China, which played an active role in the fight against the pneumonia caused by the novel coronavirus. How to make TCM benefit the general public, and to get the prescriptions of the prestigious Chinese physician without leaving home is a problem worth studying. This is of great significance for the inheritance of TCM. In this paper, an intelligent prescribing model is designed based on the nosiy-or Bayesian network. This model uses the correlation analysis method based on information entropy to obtain the network structure. The entire process greatly reduces the guidance of domain experts. The model uses the medical record data of a prestigious Chinese physician as training data, which can realize intelligent output Chinese medicine prescriptions with input symptom groups. The experimental results show a high accuracy of the model, and it can correctly simulate the diagnosis and treatment process of the prestigious Chinese physician.",
"fno": "813800a485",
"keywords": [
"Belief Networks",
"Diseases",
"Entropy",
"Medical Computing",
"Intelligent Prescribing Model",
"Traditional Chinese Medicine Prescription Model",
"Noisy Or Bayesian Network",
"Information Entropy",
"Pneumonia",
"Coronavirus",
"Bayes Methods",
"Probability Distribution",
"Medical Diagnostic Imaging",
"Noise Measurement",
"Correlation Coefficient",
"Medical Services",
"Data Preprocessing",
"Bayesian Network",
"Information Entropy",
"Symptom",
"Herb"
],
"authors": [
{
"affiliation": "Communication University of China,School of Information and Communication Engineering,Beijing,China",
"fullName": "Zhaohui Li",
"givenName": "Zhaohui",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Chinese Academy of Medical Sciences P. R. China,Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine,Department of TCM,Beijing,China",
"fullName": "Xiaogang Wang",
"givenName": "Xiaogang",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Communication University of China,School of Information and Communication Engineering,Beijing,China",
"fullName": "Wan Qiu",
"givenName": "Wan",
"surname": "Qiu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Communication University of China,School of Information and Communication Engineering,Beijing,China",
"fullName": "Dongxin Shi",
"givenName": "Dongxin",
"surname": "Shi",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccst",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-10-01T00:00:00",
"pubType": "proceedings",
"pages": "485-489",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-8138-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "813800a480",
"articleId": "1p1goXNBrNK",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "813800a490",
"articleId": "1p1gsDT9g1W",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/itme/2015/8302/0/8302a229",
"title": "Semantic Web for Knowledge Integration between Traditional Chinese Medicine and Biomedicine",
"doi": null,
"abstractUrl": "/proceedings-article/itme/2015/8302a229/12OmNCcKQj8",
"parentPublication": {
"id": "proceedings/itme/2015/8302/0",
"title": "2015 7th International Conference on Information Technology in Medicine and Education (ITME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2010/8303/0/05703877",
"title": "An ontology based knowledge management system architecture for Traditional Chinese Medicine",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703877/12OmNCuVaAm",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2013/1309/0/06732697",
"title": "Customized management of clinical data in traditional Chinese medicine",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2013/06732697/12OmNqG0SVT",
"parentPublication": {
"id": "proceedings/bibm/2013/1309/0",
"title": "2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2017/3050/0/08217857",
"title": "The ontology-based knowledge representation modeling of the traditional-Chinese-medicine symptom",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2017/08217857/12OmNxbW4PW",
"parentPublication": {
"id": "proceedings/bibm/2017/3050/0",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2010/8303/0/05703879",
"title": "Traditional Chinese Medicine data mining: Associating clinical cancer case studies with Traditional Chinese Medicine therapies",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703879/12OmNyrIaxR",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2010/8303/0/05703869",
"title": "Goal based bisimulation for testing therapies in traditional Chinese medicine",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2010/05703869/12OmNyuy9Ox",
"parentPublication": {
"id": "proceedings/bibmw/2010/8303/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2013/1309/0/06732672",
"title": "LEVIS: A hypertension dataset in traditional Chinese medicine",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2013/06732672/12OmNzYNN3n",
"parentPublication": {
"id": "proceedings/bibm/2013/1309/0",
"title": "2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2019/0858/0/09006479",
"title": "Big data and traditional Chinese medicine (TCM): What’s state of the art?",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2019/09006479/1hJsjO7elK8",
"parentPublication": {
"id": "proceedings/big-data/2019/0858/0",
"title": "2019 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icss/2020/8531/0/853100a064",
"title": "Traditional Chinese Medicine knowledge Service based on Semi-Supervised BERT-BiLSTM-CRF Model",
"doi": null,
"abstractUrl": "/proceedings-article/icss/2020/853100a064/1pDrbp8l5sI",
"parentPublication": {
"id": "proceedings/icss/2020/8531/0",
"title": "2020 International Conference on Service Science (ICSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313363",
"title": "Research on Structured Information Extraction Method of Electronic Medical Records of Traditional Chinese Medicine",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313363/1qmggdQxH32",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1rUIVdcRaJW",
"title": "2020 IEEE International Conference on Healthcare Informatics (ICHI)",
"acronym": "ichi",
"groupId": "1803080",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1rUJ1pjOWIM",
"doi": "10.1109/ICHI48887.2020.9374327",
"title": "Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis",
"normalizedTitle": "Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis",
"abstract": "Bayesian network (BN) models have been widely applied in medical diagnosis. These models can be built from different sources, including both data and domain knowledge in the form of expertise and literature. Although it might seem simple to depend only on data, this will not be the best approach unless a large dataset is available. In this study, we present a knowledge-based BN modelling approach which we applied for diagnosing the chronic disease of rheumatoid arthritis (RA). We illustrate the process of extracting the relevant knowledge, starting by identifying the BN variables implied by the activities and decision points shown in a model of the caremap for RA diagnosis. To complete this, further medical knowledge is elicited from an expert panel of rheumatologists, the medical literature is investigated, and a data set is used to parameterise the model. We compare the performance of this knowledge-based BN with another BN model learnt entirely from data. The results show that our proposed knowledge-based model outperforms the data-driven one.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bayesian network (BN) models have been widely applied in medical diagnosis. These models can be built from different sources, including both data and domain knowledge in the form of expertise and literature. Although it might seem simple to depend only on data, this will not be the best approach unless a large dataset is available. In this study, we present a knowledge-based BN modelling approach which we applied for diagnosing the chronic disease of rheumatoid arthritis (RA). We illustrate the process of extracting the relevant knowledge, starting by identifying the BN variables implied by the activities and decision points shown in a model of the caremap for RA diagnosis. To complete this, further medical knowledge is elicited from an expert panel of rheumatologists, the medical literature is investigated, and a data set is used to parameterise the model. We compare the performance of this knowledge-based BN with another BN model learnt entirely from data. The results show that our proposed knowledge-based model outperforms the data-driven one.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Bayesian network (BN) models have been widely applied in medical diagnosis. These models can be built from different sources, including both data and domain knowledge in the form of expertise and literature. Although it might seem simple to depend only on data, this will not be the best approach unless a large dataset is available. In this study, we present a knowledge-based BN modelling approach which we applied for diagnosing the chronic disease of rheumatoid arthritis (RA). We illustrate the process of extracting the relevant knowledge, starting by identifying the BN variables implied by the activities and decision points shown in a model of the caremap for RA diagnosis. To complete this, further medical knowledge is elicited from an expert panel of rheumatologists, the medical literature is investigated, and a data set is used to parameterise the model. We compare the performance of this knowledge-based BN with another BN model learnt entirely from data. The results show that our proposed knowledge-based model outperforms the data-driven one.",
"fno": "09374327",
"keywords": [
"Bayes Methods",
"Belief Networks",
"Diseases",
"Knowledge Based Systems",
"Patient Diagnosis",
"Causal Bayesian Networks",
"Medical Diagnosis",
"Rheumatoid Arthritis",
"Bayesian Network Models",
"Domain Knowledge",
"Knowledge Based BN Modelling Approach",
"Chronic Disease",
"BN Variables",
"RA Diagnosis",
"Medical Knowledge",
"Medical Literature",
"Data Set",
"BN Model",
"Knowledge Based Model",
"Analytical Models",
"Knowledge Based Systems",
"Data Models",
"Arthritis",
"Bayes Methods",
"Medical Diagnosis",
"Medical Diagnostic Imaging",
"Bayesian Networks",
"Chronic Diseases",
"Rheumatoid Arthritis",
"Diagnosis"
],
"authors": [
{
"affiliation": "Queen Mary University of London,School of Electronic Engineering and Computer Science,London,UK",
"fullName": "Ali Fahmi",
"givenName": "Ali",
"surname": "Fahmi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Queen Mary University of London,The William Harvey Institute,London,UK",
"fullName": "Amy MacBrayne",
"givenName": "Amy",
"surname": "MacBrayne",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Queen Mary University of London,School of Electronic Engineering and Computer Science,London,UK",
"fullName": "Evangelia Kyrimi",
"givenName": "Evangelia",
"surname": "Kyrimi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Queen Mary University of London,School of Electronic Engineering and Computer Science,London,UK",
"fullName": "Scott McLachlan",
"givenName": "Scott",
"surname": "McLachlan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Queen Mary University of London,The William Harvey Institute,London,UK",
"fullName": "Frances Humby",
"givenName": "Frances",
"surname": "Humby",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Queen Mary University of London,School of Electronic Engineering and Computer Science,London,UK",
"fullName": "William Marsh",
"givenName": "William",
"surname": "Marsh",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Queen Mary University of London,The William Harvey Institute,London,UK",
"fullName": "Costantino Pitzalis",
"givenName": "Costantino",
"surname": "Pitzalis",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ichi",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-11-01T00:00:00",
"pubType": "proceedings",
"pages": "1-7",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-5382-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09374397",
"articleId": "1rUIWVO5jm8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09374300",
"articleId": "1rUJ10yb31K",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2014/5669/0/06999203",
"title": "Exploring potential therapeutic agents of Duhuo-Jisheng-Tang for rheumatoid arthritis",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999203/12OmNCf1DrE",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999366",
"title": "Fang-Feng-Tang's potential therapeutic agents for rheumatoid arthritis",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999366/12OmNqI04Ja",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2013/1309/0/06732651",
"title": "Network analysis for anti-rheumatoid arthritis TCM prescriptions",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2013/06732651/12OmNvqW6YV",
"parentPublication": {
"id": "proceedings/bibm/2013/1309/0",
"title": "2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icnc/2013/5287/0/06504061",
"title": "Computer aided rehabilitation for patients with rheumatoid arthritis",
"doi": null,
"abstractUrl": "/proceedings-article/icnc/2013/06504061/12OmNy87QvI",
"parentPublication": {
"id": "proceedings/icnc/2013/5287/0",
"title": "2013 International Conference on Computing, Networking and Communications (ICNC 2013)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2014/5666/0/07004393",
"title": "Protective effects of rheumatoid arthritis in septic ICU patients",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2014/07004393/12OmNyOq4Sc",
"parentPublication": {
"id": "proceedings/big-data/2014/5666/0",
"title": "2014 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999280",
"title": "Exploring the potential therapeutic mechanism of Da-Fang-Feng-Tang for rheumatoid arthritis",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999280/12OmNybfraM",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2013/1309/0/06732769",
"title": "Exploring the molecular mechanism of Juan-Bi-Tang for Feng-Han-Shi-Bi syndrome in rheumatoid arthritis",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2013/06732769/12OmNzC5SDb",
"parentPublication": {
"id": "proceedings/bibm/2013/1309/0",
"title": "2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669149",
"title": "BuYang-HuanWu-Tang Alleviates Rheumatoid Arthritis’ Hypoxia via BNIP3 and PI3K/ATK",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669149/1A9VRePE2gE",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754243",
"title": "Visual Assistance in Development and Validation of Bayesian Networks for Clinical Decision Support",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754243/1CpcDU5uTsY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2022/6819/0/09995524",
"title": "The Chinese Medicines of Integrated Therapies Against Rheumatoid Arthritis Retard Osteoporosis",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2022/09995524/1JC2VZf82vC",
"parentPublication": {
"id": "proceedings/bibm/2022/6819/0",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1A9VchbY4Mw",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1A9VXybnCTu",
"doi": "10.1109/BIBM52615.2021.9669328",
"title": "Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting",
"normalizedTitle": "Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting",
"abstract": "With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets.",
"fno": "09669328",
"keywords": [
"Bayes Methods",
"Diseases",
"Sensor Fusion",
"Statistical Analysis",
"Time Series",
"Multimodal Information Fusion Powered Regional Covid 19",
"Current Raging Spread",
"COVID 19",
"Future Epidemic Trend",
"Public Health Security",
"Outbreak",
"Multimodal Data",
"Time Series Data",
"Disease Spread",
"Multimodal Informatio N Fusion COVID 19 Epidemic Forecasting Model",
"Intra Regional Multimodal Information",
"COVID 19 Spread",
"COVID 19",
"Epidemics",
"Correlation",
"Fuses",
"Biological System Modeling",
"Network Analyzers",
"Predictive Models",
"Multi Modal Information Fusion",
"COVID 19 Forecasting",
"Graph Neural Network",
"Self Attention",
"Social Network Analysis"
],
"authors": [
{
"affiliation": "Shandong University,School of Software,China",
"fullName": "Honglu Zhang",
"givenName": "Honglu",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong University,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR),China",
"fullName": "Yonghui Xu",
"givenName": "Yonghui",
"surname": "Xu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong University,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR),China",
"fullName": "Lei Liu",
"givenName": "Lei",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong University,School of Software,China",
"fullName": "Xudong Lu",
"givenName": "Xudong",
"surname": "Lu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong University,School of Software,China",
"fullName": "Xijie Lin",
"givenName": "Xijie",
"surname": "Lin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong University,School of Software,China",
"fullName": "Zhongmin Yan",
"givenName": "Zhongmin",
"surname": "Yan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Shandong University,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR),China",
"fullName": "Lizhen Cui",
"givenName": "Lizhen",
"surname": "Cui",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanyang Techonlogical University,Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly,Singapore",
"fullName": "Chunyan Miao",
"givenName": "Chunyan",
"surname": "Miao",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bibm",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-12-01T00:00:00",
"pubType": "proceedings",
"pages": "779-784",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-0126-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09669379",
"articleId": "1A9VvOS9YJi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09669594",
"articleId": "1A9VqkbP6Cs",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icitbe/2021/0099/0/009900a258",
"title": "Analysis and prediction of COVID-19 based on the SIR-B model",
"doi": null,
"abstractUrl": "/proceedings-article/icitbe/2021/009900a258/1AH7PniMiA0",
"parentPublication": {
"id": "proceedings/icitbe/2021/0099/0",
"title": "2021 International Conference on Information Technology and Biomedical Engineering (ICITBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aemcse/2022/8474/0/847400a272",
"title": "Research on ship tracking and control for COVID-19 epidemic prevention based on multi-source information fusion",
"doi": null,
"abstractUrl": "/proceedings-article/aemcse/2022/847400a272/1IlOeCICav6",
"parentPublication": {
"id": "proceedings/aemcse/2022/8474/0",
"title": "2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dasc-picom-cbdcom-cyberscitech/2022/6297/0/09927898",
"title": "Exploiting mobility data to forecast Covid-19 spread",
"doi": null,
"abstractUrl": "/proceedings-article/dasc-picom-cbdcom-cyberscitech/2022/09927898/1J4Cty6oXPq",
"parentPublication": {
"id": "proceedings/dasc-picom-cbdcom-cyberscitech/2022/6297/0",
"title": "2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icuems/2020/8832/0/09151826",
"title": "Study on Epidemic Prevention and Control Strategy of COVID -19 Based on Personnel Flow Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/icuems/2020/09151826/1lRlPHkznna",
"parentPublication": {
"id": "proceedings/icuems/2020/8832/0",
"title": "2020 International Conference on Urban Engineering and Management Science (ICUEMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2021/01/09234698",
"title": "An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak",
"doi": null,
"abstractUrl": "/journal/bd/2021/01/09234698/1o6H85YnhZe",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a086",
"title": "Features of Taiwan’s Public Health System Revealed in the Covid-19 Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a086/1rxhoREH7IQ",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigdia/2020/2232/0/223200a354",
"title": "Data-driven Network Model of COVID-19 Epidemic in Italy",
"doi": null,
"abstractUrl": "/proceedings-article/bigdia/2020/223200a354/1stvxHw4RVu",
"parentPublication": {
"id": "proceedings/bigdia/2020/2232/0",
"title": "2020 6th International Conference on Big Data and Information Analytics (BigDIA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cds/2021/0428/0/042800a064",
"title": "Data-driven COVID-19 growth prediction",
"doi": null,
"abstractUrl": "/proceedings-article/cds/2021/042800a064/1uZxwpLhI2c",
"parentPublication": {
"id": "proceedings/cds/2021/0428/0",
"title": "2021 2nd International Conference on Computing and Data Science (CDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icci*cc/2020/9594/0/09450226",
"title": "Simulation Analysis of Epidemic Trend for COVID-19 Based on SEIRS Model",
"doi": null,
"abstractUrl": "/proceedings-article/icci*cc/2020/09450226/1uqFPA4nCJW",
"parentPublication": {
"id": "proceedings/icci*cc/2020/9594/0",
"title": "2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscipt/2021/4137/0/413700a502",
"title": "Exploratory Data Analysis on the Usage of COVID-19 Vaccine",
"doi": null,
"abstractUrl": "/proceedings-article/iscipt/2021/413700a502/1zzpDAPfdKM",
"parentPublication": {
"id": "proceedings/iscipt/2021/4137/0",
"title": "2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1E2vX1vyvqU",
"title": "2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)",
"acronym": "iccsmt",
"groupId": "1840604",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1E2wcOKPgIg",
"doi": "10.1109/ICCSMT54525.2021.00073",
"title": "Research on the mechanism of epidemic network emergencies based on grounded theory",
"normalizedTitle": "Research on the mechanism of epidemic network emergencies based on grounded theory",
"abstract": "The purpose of studying the mechanism of epidemic situation-related network emergency in the context of COVID-19 is to provide scientific guidance for preventing epidemic situation-related network emergency, resolving the social risks caused by epidemic situation-related conflicts and maintaining social stability. Using the grounded theory method, the authors make Open Coding, Axial Coding, Selective Coding and Saturation test, and then get five main categories and four core categories. On this basis, we establish a conceptual model of the epidemic situation-related network emergency happening mechanism. The research finds that measures of prevention is the stimulus factor of epidemic situation-related network emergency. Network media is the media factor of epidemic situation-related network emergency. Social cognition is the intermediary variable of epidemic situation-related network emergency. Group psychology and group interest are the direct driving force of epidemic situation-related network emergency. Accordingly, we bring up some beneficial countermeasures and suggestions for the government emergency management to prevent, resolve and control the epidemic situation-related network emergency.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The purpose of studying the mechanism of epidemic situation-related network emergency in the context of COVID-19 is to provide scientific guidance for preventing epidemic situation-related network emergency, resolving the social risks caused by epidemic situation-related conflicts and maintaining social stability. Using the grounded theory method, the authors make Open Coding, Axial Coding, Selective Coding and Saturation test, and then get five main categories and four core categories. On this basis, we establish a conceptual model of the epidemic situation-related network emergency happening mechanism. The research finds that measures of prevention is the stimulus factor of epidemic situation-related network emergency. Network media is the media factor of epidemic situation-related network emergency. Social cognition is the intermediary variable of epidemic situation-related network emergency. Group psychology and group interest are the direct driving force of epidemic situation-related network emergency. Accordingly, we bring up some beneficial countermeasures and suggestions for the government emergency management to prevent, resolve and control the epidemic situation-related network emergency.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The purpose of studying the mechanism of epidemic situation-related network emergency in the context of COVID-19 is to provide scientific guidance for preventing epidemic situation-related network emergency, resolving the social risks caused by epidemic situation-related conflicts and maintaining social stability. Using the grounded theory method, the authors make Open Coding, Axial Coding, Selective Coding and Saturation test, and then get five main categories and four core categories. On this basis, we establish a conceptual model of the epidemic situation-related network emergency happening mechanism. The research finds that measures of prevention is the stimulus factor of epidemic situation-related network emergency. Network media is the media factor of epidemic situation-related network emergency. Social cognition is the intermediary variable of epidemic situation-related network emergency. Group psychology and group interest are the direct driving force of epidemic situation-related network emergency. Accordingly, we bring up some beneficial countermeasures and suggestions for the government emergency management to prevent, resolve and control the epidemic situation-related network emergency.",
"fno": "206300a363",
"keywords": [
"Cognition",
"Diseases",
"Emergency Management",
"Epidemics",
"Health Care",
"Internet",
"Psychology",
"Risk Management",
"Social Networking Online",
"Epidemic Network Emergencies",
"Grounded Theory",
"COVID 19",
"Network Media",
"Social Cognition",
"Group Psychology",
"Group Interest",
"Government Emergency Management",
"COVID 19",
"Epidemics",
"Government",
"Force",
"Psychology",
"Media",
"Emergency Services",
"Outbreak Of COVID 19",
"Network Emergency",
"Grounded Theory",
"Occurring Mechanism",
"Emergency Management"
],
"authors": [
{
"affiliation": "School of Economics & Management, Harbin Engineering University,Harbin,China",
"fullName": "JinGui Jiang",
"givenName": "JinGui",
"surname": "Jiang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Economics & Management, Harbin Engineering University,Harbin,China",
"fullName": "GuoMei Meng",
"givenName": "GuoMei",
"surname": "Meng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Economics & Management, Harbin Engineering University,Harbin,China",
"fullName": "Feng Lu",
"givenName": "Feng",
"surname": "Lu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccsmt",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-11-01T00:00:00",
"pubType": "proceedings",
"pages": "363-371",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-2063-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "206300a358",
"articleId": "1E2w9xQtfSU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "206300a372",
"articleId": "1E2w4c5VMxW",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tg/5555/01/09750868",
"title": "EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09750868/1ClSREG2DeM",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2022/6819/0/09995301",
"title": "Mechanism of Cold-Dampness Epidemic Prescription in Treatment of Novel Coronavirus Pneumonia Based on Network Pharmacology",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2022/09995301/1JC1YKuaP7O",
"parentPublication": {
"id": "proceedings/bibm/2022/6819/0",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icftic/2022/2195/0/10075291",
"title": "Design of Student Information Management System for Chinese University in Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/icftic/2022/10075291/1LRlbsX5r1e",
"parentPublication": {
"id": "proceedings/icftic/2022/2195/0",
"title": "2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icuems/2020/8832/0/09151826",
"title": "Study on Epidemic Prevention and Control Strategy of COVID -19 Based on Personnel Flow Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/icuems/2020/09151826/1lRlPHkznna",
"parentPublication": {
"id": "proceedings/icuems/2020/8832/0",
"title": "2020 International Conference on Urban Engineering and Management Science (ICUEMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvidl/2020/9481/0/948100a542",
"title": "Investigation on the Difficulties and Challenges of Teachers Online Teaching in Primary and Middle Schools of Guangxi Middle School",
"doi": null,
"abstractUrl": "/proceedings-article/cvidl/2020/948100a542/1pbe8SR3IVa",
"parentPublication": {
"id": "proceedings/cvidl/2020/9481/0",
"title": "2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaice/2020/9146/0/914600a506",
"title": "Transmission mechanism of Novel coronavirus based on SIR model and emergency supplies network’s relation",
"doi": null,
"abstractUrl": "/proceedings-article/icaice/2020/914600a506/1rCgbyPp3JC",
"parentPublication": {
"id": "proceedings/icaice/2020/9146/0",
"title": "2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a086",
"title": "Features of Taiwan’s Public Health System Revealed in the Covid-19 Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a086/1rxhoREH7IQ",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaml/2020/9264/0/926400a103",
"title": "Design and research of novel coronavirus epidemic screening system",
"doi": null,
"abstractUrl": "/proceedings-article/icaml/2020/926400a103/1yLP3tpFADm",
"parentPublication": {
"id": "proceedings/icaml/2020/9264/0",
"title": "2020 2nd International Conference on Applied Machine Learning (ICAML)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2021/2594/0/259400a153",
"title": "Research on Digital Government Governance in Public Health Emergencies",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2021/259400a153/1ymIMvXTbvW",
"parentPublication": {
"id": "proceedings/icphds/2021/2594/0",
"title": "2021 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2021/2594/0/259400a158",
"title": "Study on tax administration design and management of incentive emergency response to public health emergencies in response to COVID-19",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2021/259400a158/1ymIQnlpOve",
"parentPublication": {
"id": "proceedings/icphds/2021/2594/0",
"title": "2021 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1J4CiR0iIBa",
"title": "2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)",
"acronym": "dasc-picom-cbdcom-cyberscitech",
"groupId": "9927523",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1J4Cty6oXPq",
"doi": "10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927898",
"title": "Exploiting mobility data to forecast Covid-19 spread",
"normalizedTitle": "Exploiting mobility data to forecast Covid-19 spread",
"abstract": "Infectious diseases are spread through human-human transmissions; thus, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents a data-driven predictive approach that analizes both mobility and infection data to discover spatio-temporal predictive epidemic patterns. Preliminary results, obtained by analyzing data related to mobility and COVID-19 infections in Chicago, show that the approach is promising.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Infectious diseases are spread through human-human transmissions; thus, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents a data-driven predictive approach that analizes both mobility and infection data to discover spatio-temporal predictive epidemic patterns. Preliminary results, obtained by analyzing data related to mobility and COVID-19 infections in Chicago, show that the approach is promising.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Infectious diseases are spread through human-human transmissions; thus, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents a data-driven predictive approach that analizes both mobility and infection data to discover spatio-temporal predictive epidemic patterns. Preliminary results, obtained by analyzing data related to mobility and COVID-19 infections in Chicago, show that the approach is promising.",
"fno": "09927898",
"keywords": [
"Data Analysis",
"Diseases",
"Epidemics",
"Medical Computing",
"COVID 19 Infections",
"Data Driven Predictive Approach",
"Epidemic Forecasting",
"Human Human Transmissions",
"Infection Data",
"Infectious Diseases",
"Mobility Data",
"Spatio Temporal Mobility Data",
"Spatio Temporal Predictive Epidemic Patterns",
"COVID 19",
"Epidemics",
"Analytical Models",
"Infectious Diseases",
"Computational Modeling",
"Measurement Uncertainty",
"Predictive Models"
],
"authors": [
{
"affiliation": "University of Calabria,Italy",
"fullName": "Maria Pia Canino",
"givenName": "Maria Pia",
"surname": "Canino",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Calabria,Italy",
"fullName": "Eugenio Cesario",
"givenName": "Eugenio",
"surname": "Cesario",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "ICAR-CNR,Italy",
"fullName": "Andrea Vinci",
"givenName": "Andrea",
"surname": "Vinci",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Monmouth University,U.S.",
"fullName": "Shabnam Zarin",
"givenName": "Shabnam",
"surname": "Zarin",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "dasc-picom-cbdcom-cyberscitech",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-09-01T00:00:00",
"pubType": "proceedings",
"pages": "1-4",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6297-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09927837",
"articleId": "1J4CxIUBcQw",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09927866",
"articleId": "1J4CsIgkW0U",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icitbe/2021/0099/0/009900a258",
"title": "Analysis and prediction of COVID-19 based on the SIR-B model",
"doi": null,
"abstractUrl": "/proceedings-article/icitbe/2021/009900a258/1AH7PniMiA0",
"parentPublication": {
"id": "proceedings/icitbe/2021/0099/0",
"title": "2021 International Conference on Information Technology and Biomedical Engineering (ICITBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iwecai/2022/7997/0/799700a512",
"title": "Forecast of COVID-19 Vaccinations in the US, India, and South Africa",
"doi": null,
"abstractUrl": "/proceedings-article/iwecai/2022/799700a512/1Cugtm3GHJe",
"parentPublication": {
"id": "proceedings/iwecai/2022/7997/0",
"title": "2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2022/4609/0/460900b189",
"title": "Human Mobility Driven Modeling of an Infectious Disease",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2022/460900b189/1KBqWHalR0k",
"parentPublication": {
"id": "proceedings/icdmw/2022/4609/0",
"title": "2022 IEEE International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/5555/01/10052720",
"title": "Epidemic Spread Modeling for COVID-19 Using Cross-fertilization of Mobility Data",
"doi": null,
"abstractUrl": "/journal/bd/5555/01/10052720/1L1HTGnZHEs",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icftic/2022/2195/0/10075276",
"title": "Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode",
"doi": null,
"abstractUrl": "/proceedings-article/icftic/2022/10075276/1LRl7WPLY5y",
"parentPublication": {
"id": "proceedings/icftic/2022/2195/0",
"title": "2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isci/2022/9631/0/963100a022",
"title": "An Evidence Study of Long-term Impacts on Mobility Patterns Brought by COVID-19",
"doi": null,
"abstractUrl": "/proceedings-article/isci/2022/963100a022/1Lz20v6kOm4",
"parentPublication": {
"id": "proceedings/isci/2022/9631/0",
"title": "2022 IEEE 10th International Conference on Smart City and Informatization (iSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2021/01/09234698",
"title": "An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak",
"doi": null,
"abstractUrl": "/journal/bd/2021/01/09234698/1o6H85YnhZe",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2020/7647/0/764700a062",
"title": "Visual Analysis and Exploration of COVID-19 Based on Multi-source Heterogeneous Data",
"doi": null,
"abstractUrl": "/proceedings-article/ithings-greencom-cpscom-smartdata-cybermatics/2020/764700a062/1pVHgLX46YM",
"parentPublication": {
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2020/7647/0",
"title": "2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaice/2020/9146/0/914600a383",
"title": "COVID-19 Spreading Prediction with Enhanced SEIR Model",
"doi": null,
"abstractUrl": "/proceedings-article/icaice/2020/914600a383/1rCgb72Z6s8",
"parentPublication": {
"id": "proceedings/icaice/2020/9146/0",
"title": "2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a247",
"title": "The Prediction of the Spread of COVID-19 using Regression Models",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a247/1rxhrAL545O",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KBqPQkw71C",
"title": "2022 IEEE International Conference on Data Mining Workshops (ICDMW)",
"acronym": "icdmw",
"groupId": "10029378",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1KBqWHalR0k",
"doi": "10.1109/ICDMW58026.2022.00155",
"title": "Human Mobility Driven Modeling of an Infectious Disease",
"normalizedTitle": "Human Mobility Driven Modeling of an Infectious Disease",
"abstract": "In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age.",
"fno": "460900b189",
"keywords": [
"Cellular Radio",
"Diseases",
"Epidemics",
"Medical Computing",
"Medical Information Systems",
"Mobile Computing",
"CBG",
"Cell Phone Data",
"Census Block Groups",
"COVID 19",
"Deterministic Epidemic Model",
"Disease Properties",
"Epidemiological Model",
"Human Behavior",
"Human Mobility Driven Modeling",
"Human Mobility Information",
"Incubation Period",
"Infection Rate",
"Infectious Disease",
"Safe Graph",
"SEIRD M",
"Susceptible Exposed Infected And Recovered",
"COVID 19",
"Epidemics",
"Infectious Diseases",
"Conferences",
"Sociology",
"Data Models",
"Behavioral Sciences",
"Infectious Diseases",
"SEIR",
"SEIRD M",
"Human Mobility"
],
"authors": [
{
"affiliation": "University of Texas at El Paso,Department of Computer Science,El Paso,Texas,United States",
"fullName": "Ismael Villanueva-Miranda",
"givenName": "Ismael",
"surname": "Villanueva-Miranda",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Texas at El Paso,Department of Computer Science,El Paso,Texas,United States",
"fullName": "M. Shahriar Hossain",
"givenName": "M. Shahriar",
"surname": "Hossain",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Texas at El Paso,Department of Computer Science,El Paso,Texas,United States",
"fullName": "Monika Akbar",
"givenName": "Monika",
"surname": "Akbar",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icdmw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-11-01T00:00:00",
"pubType": "proceedings",
"pages": "1189-1196",
"year": "2022",
"issn": null,
"isbn": "979-8-3503-4609-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "460900b181",
"articleId": "1KBr1oG6Q8g",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "460900b197",
"articleId": "1KBr2hkaYGk",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cse-euc/2016/3593/0/07982317",
"title": "A Study on Perception of Managing Infectious Disease through Social Networking in the Kingdom of Saudi Arabia",
"doi": null,
"abstractUrl": "/proceedings-article/cse-euc/2016/07982317/17D45Xtvpc6",
"parentPublication": {
"id": "proceedings/cse-euc/2016/3593/0",
"title": "2016 19th IEEE Intl Conference on Computational Science and Engineering (CSE), IEEE 14th Intl Conference on Embedded and Ubiquitous Computing (EUC), and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2018/9288/0/928800b417",
"title": "IDDAT: An Ontology-Driven Decision Support System for Infectious Disease Diagnosis and Therapy",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2018/928800b417/18jXH11TPYQ",
"parentPublication": {
"id": "proceedings/icdmw/2018/9288/0",
"title": "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aipr/2021/2471/0/09762205",
"title": "Anonymized Blockchain-Based Infection Tracking for Disease Control",
"doi": null,
"abstractUrl": "/proceedings-article/aipr/2021/09762205/1CT9bXEc0x2",
"parentPublication": {
"id": "proceedings/aipr/2021/2471/0",
"title": "2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09750868",
"title": "EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09750868/1ClSREG2DeM",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mdm/2022/5176/0/517600a292",
"title": "A Mobility-based Recommendation System for Mitigating the Risk of Infection during Epidemics",
"doi": null,
"abstractUrl": "/proceedings-article/mdm/2022/517600a292/1G89RV7GZCo",
"parentPublication": {
"id": "proceedings/mdm/2022/5176/0",
"title": "2022 23rd IEEE International Conference on Mobile Data Management (MDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dasc-picom-cbdcom-cyberscitech/2022/6297/0/09927898",
"title": "Exploiting mobility data to forecast Covid-19 spread",
"doi": null,
"abstractUrl": "/proceedings-article/dasc-picom-cbdcom-cyberscitech/2022/09927898/1J4Cty6oXPq",
"parentPublication": {
"id": "proceedings/dasc-picom-cbdcom-cyberscitech/2022/6297/0",
"title": "2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10020895",
"title": "Shape-based Evaluation of Epidemic Forecasts",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10020895/1KfSsZgve5G",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/asonam/2022/5661/0/10068686",
"title": "Quarantine in Motion: A Graph Learning Framework to Reduce Disease Transmission Without Lockdown",
"doi": null,
"abstractUrl": "/proceedings-article/asonam/2022/10068686/1LKx2S41yBa",
"parentPublication": {
"id": "proceedings/asonam/2022/5661/0",
"title": "2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigdia/2020/2232/0/223200a354",
"title": "Data-driven Network Model of COVID-19 Epidemic in Italy",
"doi": null,
"abstractUrl": "/proceedings-article/bigdia/2020/223200a354/1stvxHw4RVu",
"parentPublication": {
"id": "proceedings/bigdia/2020/2232/0",
"title": "2020 6th International Conference on Big Data and Information Analytics (BigDIA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2021/2594/0/259400a263",
"title": "GIS-based characteristics of Infectious disease transmission: A comparison of COVID-19 and SARS in Guangzhou, China",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2021/259400a263/1ymIM84b4xa",
"parentPublication": {
"id": "proceedings/icphds/2021/2594/0",
"title": "2021 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KBqPQkw71C",
"title": "2022 IEEE International Conference on Data Mining Workshops (ICDMW)",
"acronym": "icdmw",
"groupId": "10029378",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1KBr1oG6Q8g",
"doi": "10.1109/ICDMW58026.2022.00093",
"title": "VISUAL ANALYTICS OF MOBILITY NETWORK CHANGES OBSERVED USING MOBILE PHONE DATA DURING COVID-19 PANDEMIC",
"normalizedTitle": "VISUAL ANALYTICS OF MOBILITY NETWORK CHANGES OBSERVED USING MOBILE PHONE DATA DURING COVID-19 PANDEMIC",
"abstract": "The limited exchange between human communities is a key factor in preventing the spread of COVID-19. This paper introduces a digital framework that combines an integration of real mobility data at the country scale with a series of modeling techniques and visual capabilities that highlight mobility patterns before and during the pandemic. The findings not only significantly exhibit mobility trends and different degrees of similarities at regional and local levels but also provide potential insight into the emergence of a pandemic on human behavior patterns and their likely socio-economic impacts.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The limited exchange between human communities is a key factor in preventing the spread of COVID-19. This paper introduces a digital framework that combines an integration of real mobility data at the country scale with a series of modeling techniques and visual capabilities that highlight mobility patterns before and during the pandemic. The findings not only significantly exhibit mobility trends and different degrees of similarities at regional and local levels but also provide potential insight into the emergence of a pandemic on human behavior patterns and their likely socio-economic impacts.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The limited exchange between human communities is a key factor in preventing the spread of COVID-19. This paper introduces a digital framework that combines an integration of real mobility data at the country scale with a series of modeling techniques and visual capabilities that highlight mobility patterns before and during the pandemic. The findings not only significantly exhibit mobility trends and different degrees of similarities at regional and local levels but also provide potential insight into the emergence of a pandemic on human behavior patterns and their likely socio-economic impacts.",
"fno": "460900b181",
"keywords": [
"Data Analysis",
"Data Visualisation",
"Diseases",
"Epidemics",
"Mobile Computing",
"Socio Economic Effects",
"COVID 19 PANDEMIC",
"Digital Framework",
"Highlight Mobility Patterns",
"Human Behavior Patterns",
"Human Communities",
"Mobility Data",
"MOBILITY NETWORK CHANGES OBSERVED USING MOBILE PHONE DATA",
"Mobility Trends",
"VISUAL ANALYTICS",
"Visual Capabilities",
"COVID 19",
"Pandemics",
"Visual Analytics",
"Conferences",
"Data Visualization",
"Market Research",
"Mobile Handsets",
"COVID 19",
"Graphs",
"Mobility Patterns",
"Flow Map",
"Visualization"
],
"authors": [
{
"affiliation": "South Tehran Branch, Islamic Azad University,Iran",
"fullName": "Mohammad Forghani",
"givenName": "Mohammad",
"surname": "Forghani",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Naval Academy Research Institute,France",
"fullName": "Christophe Claramunt",
"givenName": "Christophe",
"surname": "Claramunt",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute of Science and Technology Austria (ISTA),Austria",
"fullName": "Farid Karimipour",
"givenName": "Farid",
"surname": "Karimipour",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Vienna University of Technology,Austria",
"fullName": "Georg Heiler",
"givenName": "Georg",
"surname": "Heiler",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icdmw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-11-01T00:00:00",
"pubType": "proceedings",
"pages": "1181-1188",
"year": "2022",
"issn": null,
"isbn": "979-8-3503-4609-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "460900b176",
"articleId": "1KBqXsmSe3u",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "460900b189",
"articleId": "1KBqWHalR0k",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/csci/2021/5841/0/584100b095",
"title": "Impact of COVID-19 pandemic on Higher Education",
"doi": null,
"abstractUrl": "/proceedings-article/csci/2021/584100b095/1EpLpJxbKyQ",
"parentPublication": {
"id": "proceedings/csci/2021/5841/0",
"title": "2021 International Conference on Computational Science and Computational Intelligence (CSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2023/01/09992065",
"title": "Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing",
"doi": null,
"abstractUrl": "/magazine/cg/2023/01/09992065/1JevLVx8ZBC",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10020497",
"title": "The relationship between Twitter sentiment and mobility during the COVID-19 pandemic",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10020497/1KfRuG4QnlK",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isci/2022/9631/0/963100a022",
"title": "An Evidence Study of Long-term Impacts on Mobility Patterns Brought by COVID-19",
"doi": null,
"abstractUrl": "/proceedings-article/isci/2022/963100a022/1Lz20v6kOm4",
"parentPublication": {
"id": "proceedings/isci/2022/9631/0",
"title": "2022 IEEE 10th International Conference on Smart City and Informatization (iSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313281",
"title": "Consumer Demand Modeling During COVID-19 Pandemic",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313281/1qmfYzauzf2",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a257",
"title": "Quantitative Factors and Mathematical Modeling of CoVID-19 Pandemic Under Human Interventions",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a257/1rxhtPm9IMU",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09378374",
"title": "Country-wide Mobility Changes Observed Using Mobile Phone Data During COVID-19 Pandemic",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09378374/1s64i3CxhOE",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377932",
"title": "Factors Influencing Human Mobility During The COVID-19 Pandemic in Selected Countries of Europe and North America",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377932/1s64jmOnGwM",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/lcn/2021/1886/0/09524884",
"title": "Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data",
"doi": null,
"abstractUrl": "/proceedings-article/lcn/2021/09524884/1wHJ22ayJoI",
"parentPublication": {
"id": "proceedings/lcn/2021/1886/0",
"title": "2021 IEEE 46th Conference on Local Computer Networks (LCN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/re/2021/2856/0/285600a106",
"title": "The Rise and Fall of COVID-19 Contact-Tracing Apps: when NFRs Collide with Pandemic",
"doi": null,
"abstractUrl": "/proceedings-article/re/2021/285600a106/1yDjKsXkaSQ",
"parentPublication": {
"id": "proceedings/re/2021/2856/0",
"title": "2021 IEEE 29th International Requirements Engineering Conference (RE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1LRl0szGl3i",
"title": "2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)",
"acronym": "icftic",
"groupId": "10073966",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1LRlbsX5r1e",
"doi": "10.1109/ICFTIC57696.2022.10075291",
"title": "Design of Student Information Management System for Chinese University in Epidemic",
"normalizedTitle": "Design of Student Information Management System for Chinese University in Epidemic",
"abstract": "The Student Information Management System in the epidemic includes developing front-end applications and setting up and maintaining the database, which was created with HTML, PHP, MySQL and other languages, combined with the web front-end and database. The architecture and the capabilities of the system are described in this paper. And the function modules of the system, the design idea of the database and the implementation of the front-end interface are shown in this paper emphatically. The fully functional and concise interface can effectively prevent the spread of the epidemic while providing a good experience for students while ensuring their health and satisfaction of students. This system is very in line with the current epidemic prevention measures of Chinese universities, which can help schools better manage students in the epidemic environment, curbing the spread of the epidemic and protecting students' health. Simultaneously, this system has good scalability, so if there is another epidemic situation in the future, this system can be used to this system can be a basis for secondary development. Other similar systems can be designed based on this system according to specific purpose application.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The Student Information Management System in the epidemic includes developing front-end applications and setting up and maintaining the database, which was created with HTML, PHP, MySQL and other languages, combined with the web front-end and database. The architecture and the capabilities of the system are described in this paper. And the function modules of the system, the design idea of the database and the implementation of the front-end interface are shown in this paper emphatically. The fully functional and concise interface can effectively prevent the spread of the epidemic while providing a good experience for students while ensuring their health and satisfaction of students. This system is very in line with the current epidemic prevention measures of Chinese universities, which can help schools better manage students in the epidemic environment, curbing the spread of the epidemic and protecting students' health. Simultaneously, this system has good scalability, so if there is another epidemic situation in the future, this system can be used to this system can be a basis for secondary development. Other similar systems can be designed based on this system according to specific purpose application.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The Student Information Management System in the epidemic includes developing front-end applications and setting up and maintaining the database, which was created with HTML, PHP, MySQL and other languages, combined with the web front-end and database. The architecture and the capabilities of the system are described in this paper. And the function modules of the system, the design idea of the database and the implementation of the front-end interface are shown in this paper emphatically. The fully functional and concise interface can effectively prevent the spread of the epidemic while providing a good experience for students while ensuring their health and satisfaction of students. This system is very in line with the current epidemic prevention measures of Chinese universities, which can help schools better manage students in the epidemic environment, curbing the spread of the epidemic and protecting students' health. Simultaneously, this system has good scalability, so if there is another epidemic situation in the future, this system can be used to this system can be a basis for secondary development. Other similar systems can be designed based on this system according to specific purpose application.",
"fno": "10075291",
"keywords": [
"Data Protection",
"Database Management Systems",
"Educational Administrative Data Processing",
"Educational Institutions",
"Epidemics",
"Hypermedia Markup Languages",
"Information Management",
"Online Front Ends",
"User Interfaces",
"Chinese University",
"Concise Interface",
"Database",
"Epidemic Prevention Measures",
"Front End Interface",
"HTML",
"My SQL",
"PHP",
"Student Health Protection",
"Student Information Management System Design",
"Student Satisfaction",
"Wb Front End",
"COVID 19",
"Epidemics",
"Databases",
"Scalability",
"Current Measurement",
"Information Management",
"Security",
"Student Information Management System",
"Epidemic",
"SQL Language",
"Database"
],
"authors": [
{
"affiliation": "Sydney Smart Technology College, Northeastern University,Qinhuangdao,China",
"fullName": "Zihan Ma",
"givenName": "Zihan",
"surname": "Ma",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computer Science & Information Engineering, Shanghai institute of technology,Shanghai,China",
"fullName": "Jinhuan Zhu",
"givenName": "Jinhuan",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icftic",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-12-01T00:00:00",
"pubType": "proceedings",
"pages": "247-255",
"year": "2022",
"issn": null,
"isbn": "979-8-3503-2195-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "10075270",
"articleId": "1LRlf34ujDi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10075271",
"articleId": "1LRl1LvUNBS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/big-data/2021/3902/0/09671629",
"title": "Course Scheduling to Minimize Student Wait Times For University Buildings During Epidemics",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2021/09671629/1A8gq5tPOFO",
"parentPublication": {
"id": "proceedings/big-data/2021/3902/0",
"title": "2021 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eitt/2021/2757/0/275700a134",
"title": "The status of college students' online learning power under the COVID-19",
"doi": null,
"abstractUrl": "/proceedings-article/eitt/2021/275700a134/1AFsmVRfpuw",
"parentPublication": {
"id": "proceedings/eitt/2021/2757/0",
"title": "2021 Tenth International Conference of Educational Innovation through Technology (EITT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccsmt/2021/2063/0/206300a363",
"title": "Research on the mechanism of epidemic network emergencies based on grounded theory",
"doi": null,
"abstractUrl": "/proceedings-article/iccsmt/2021/206300a363/1E2wcOKPgIg",
"parentPublication": {
"id": "proceedings/iccsmt/2021/2063/0",
"title": "2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icekim/2022/1666/0/166600a996",
"title": "Meta analysis of mental health status of Chinese college students during new coronavirus pneumonia",
"doi": null,
"abstractUrl": "/proceedings-article/icekim/2022/166600a996/1KpBLDQ8kuc",
"parentPublication": {
"id": "proceedings/icekim/2022/1666/0",
"title": "2022 3rd International Conference on Education, Knowledge and Information Management (ICEKIM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmeim/2020/9623/0/962300a275",
"title": "The legitimacy of the Contactless teaching influence on student's learning efficiency in colleges and universities: From COVID- 19 epidemic perspective",
"doi": null,
"abstractUrl": "/proceedings-article/icmeim/2020/962300a275/1syvpgtH5zW",
"parentPublication": {
"id": "proceedings/icmeim/2020/9623/0",
"title": "2020 International Conference on Modern Education and Information Management (ICMEIM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pmis/2021/3872/0/387200a041",
"title": "Analysis on Public Crisis Management Issues and Decision-making Based on Big Data in the Post-Epidemic Era",
"doi": null,
"abstractUrl": "/proceedings-article/pmis/2021/387200a041/1t2n0P44MRG",
"parentPublication": {
"id": "proceedings/pmis/2021/3872/0",
"title": "2021 International Conference on Public Management and Intelligent Society (PMIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wi-iat/2020/1924/0/192400a708",
"title": "Study on the Influencing Factors of Chinese College Students’ Online Learning Effect During the COVID-19 Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/wi-iat/2020/192400a708/1uHhxt6FoGY",
"parentPublication": {
"id": "proceedings/wi-iat/2020/1924/0",
"title": "2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieit/2021/2563/0/256300a304",
"title": "The Integration of Spirit of Combating the COVID-19 Epidemic into Ideological and Political Education in Universities under the background of Internet",
"doi": null,
"abstractUrl": "/proceedings-article/ieit/2021/256300a304/1wHKuPxFW0M",
"parentPublication": {
"id": "proceedings/ieit/2021/2563/0",
"title": "2021 International Conference on Internet, Education and Information Technology (IEIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaml/2020/9264/0/926400a103",
"title": "Design and research of novel coronavirus epidemic screening system",
"doi": null,
"abstractUrl": "/proceedings-article/icaml/2020/926400a103/1yLP3tpFADm",
"parentPublication": {
"id": "proceedings/icaml/2020/9264/0",
"title": "2020 2nd International Conference on Applied Machine Learning (ICAML)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cipae/2021/2665/0/266500a244",
"title": "The Transformation and Development of Chinese Legal Education Model under the Background of the COVID-19 Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/cipae/2021/266500a244/1yQB3DPvaCc",
"parentPublication": {
"id": "proceedings/cipae/2021/2665/0",
"title": "2021 International Conference on Computers, Information Processing and Advanced Education (CIPAE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1lRlN7oaMhi",
"title": "2020 International Conference on Urban Engineering and Management Science (ICUEMS)",
"acronym": "icuems",
"groupId": "1837364",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1lRlPHkznna",
"doi": "10.1109/ICUEMS50872.2020.00150",
"title": "Study on Epidemic Prevention and Control Strategy of COVID -19 Based on Personnel Flow Prediction",
"normalizedTitle": "Study on Epidemic Prevention and Control Strategy of COVID -19 Based on Personnel Flow Prediction",
"abstract": "In this paper, a COVID-19 risk prevention and control decision-making model is proposed according to the incompatible characteristics of epidemic risk prediction. Firstly, the uncertainty attribute of epidemic risk was analyzed through the collection of information on the personnel flow, and the problem that the risk of the epidemic could not be accurately predicted due to the uncertainty of the personnel flow was solved, and emergency prevention and control countermeasures were proposed for the possible COVID-19. Secondly, a model of epidemic risk prevention and control analysis is established by using correlation function method, which provides a new research method for risk prevention and control of public health security.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, a COVID-19 risk prevention and control decision-making model is proposed according to the incompatible characteristics of epidemic risk prediction. Firstly, the uncertainty attribute of epidemic risk was analyzed through the collection of information on the personnel flow, and the problem that the risk of the epidemic could not be accurately predicted due to the uncertainty of the personnel flow was solved, and emergency prevention and control countermeasures were proposed for the possible COVID-19. Secondly, a model of epidemic risk prevention and control analysis is established by using correlation function method, which provides a new research method for risk prevention and control of public health security.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, a COVID-19 risk prevention and control decision-making model is proposed according to the incompatible characteristics of epidemic risk prediction. Firstly, the uncertainty attribute of epidemic risk was analyzed through the collection of information on the personnel flow, and the problem that the risk of the epidemic could not be accurately predicted due to the uncertainty of the personnel flow was solved, and emergency prevention and control countermeasures were proposed for the possible COVID-19. Secondly, a model of epidemic risk prevention and control analysis is established by using correlation function method, which provides a new research method for risk prevention and control of public health security.",
"fno": "09151826",
"keywords": [
"Decision Making",
"Diseases",
"Risk Management",
"Epidemic Prevention",
"Personnel Flow Prediction",
"Control Decision Making Model",
"Epidemic Risk Prediction",
"Emergency Prevention",
"Epidemic Risk Prevention",
"COVID 19 Risk Prevention",
"Decision Making Model",
"COVID 19 Risk Control",
"Personnel",
"Monitoring",
"Forecasting",
"Epidemics",
"COVID 19",
"Predictive Models",
"Decision Making",
"COVID 19",
"Personnel Flow Prediction",
"Epidemic Prevention And Control Strategy"
],
"authors": [
{
"affiliation": "Research Center of Social Governance and Police Modernization Liaoning Police College,Dalian, Liaoning,China",
"fullName": "Ping He",
"givenName": "Ping",
"surname": "He",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icuems",
"isOpenAccess": true,
"showRecommendedArticles": true,
"showBuyMe": false,
"hasPdf": true,
"pubDate": "2020-04-01T00:00:00",
"pubType": "proceedings",
"pages": "688-691",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-8832-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09151603",
"articleId": "1lRlV01MoX6",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09151730",
"articleId": "1lRlWIpnNkc",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2021/0126/0/09669328",
"title": "Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669328/1A9VXybnCTu",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaice/2021/2186/0/218600a408",
"title": "Simulation of prevention and control of COVID-19 epidemic based on computer modeling",
"doi": null,
"abstractUrl": "/proceedings-article/icaice/2021/218600a408/1Et4zS2fInm",
"parentPublication": {
"id": "proceedings/icaice/2021/2186/0",
"title": "2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aemcse/2022/8474/0/847400a272",
"title": "Research on ship tracking and control for COVID-19 epidemic prevention based on multi-source information fusion",
"doi": null,
"abstractUrl": "/proceedings-article/aemcse/2022/847400a272/1IlOeCICav6",
"parentPublication": {
"id": "proceedings/aemcse/2022/8474/0",
"title": "2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2022/6819/0/09995286",
"title": "Application research of “5G + Chinese medicine service” in the prevention and control of COVID-19 epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2022/09995286/1JC2qizo2lO",
"parentPublication": {
"id": "proceedings/bibm/2022/6819/0",
"title": "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icftic/2022/2195/0/10075276",
"title": "Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode",
"doi": null,
"abstractUrl": "/proceedings-article/icftic/2022/10075276/1LRl7WPLY5y",
"parentPublication": {
"id": "proceedings/icftic/2022/2195/0",
"title": "2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a086",
"title": "Features of Taiwan’s Public Health System Revealed in the Covid-19 Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a086/1rxhoREH7IQ",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a278",
"title": "Design and Development of a Visualization System for COVID-19 Simulation Based on WebGIS",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a278/1rxhqjoMdpu",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a116",
"title": "Western perspectives of the COVID-19 in China",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a116/1rxhqmRWa0o",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigdia/2020/2232/0/223200a354",
"title": "Data-driven Network Model of COVID-19 Epidemic in Italy",
"doi": null,
"abstractUrl": "/proceedings-article/bigdia/2020/223200a354/1stvxHw4RVu",
"parentPublication": {
"id": "proceedings/bigdia/2020/2232/0",
"title": "2020 6th International Conference on Big Data and Information Analytics (BigDIA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icci*cc/2020/9594/0/09450226",
"title": "Simulation Analysis of Epidemic Trend for COVID-19 Based on SEIRS Model",
"doi": null,
"abstractUrl": "/proceedings-article/icci*cc/2020/09450226/1uqFPA4nCJW",
"parentPublication": {
"id": "proceedings/icci*cc/2020/9594/0",
"title": "2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1rxhoGDb2vu",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"acronym": "icphds",
"groupId": "1840485",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1rxhoREH7IQ",
"doi": "10.1109/ICPHDS51617.2020.00025",
"title": "Features of Taiwan’s Public Health System Revealed in the Covid-19 Epidemic",
"normalizedTitle": "Features of Taiwan’s Public Health System Revealed in the Covid-19 Epidemic",
"abstract": "Since the start of 2020, Covid-19 has been wreaking havoc around the world, posing threats to economic growth and people's lives. Through literature research, this study first studied on laws and regulations of public health, and summarized the normalized epidemic control policies of Taiwan. Later, by sorting news and administrative documents on Covid-19 control in Taiwan, this study analyzed features on the system of early warning, risk control, border quarantine inspection, medical work, and security of materials. Research results show that the good public health system in Taiwan has provided a strong support for containment of the Covid-19 epidemic. This study is expected to provide some experience for public health construction of Chinese Mainland to improve Chinese emergency management capacity.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Since the start of 2020, Covid-19 has been wreaking havoc around the world, posing threats to economic growth and people's lives. Through literature research, this study first studied on laws and regulations of public health, and summarized the normalized epidemic control policies of Taiwan. Later, by sorting news and administrative documents on Covid-19 control in Taiwan, this study analyzed features on the system of early warning, risk control, border quarantine inspection, medical work, and security of materials. Research results show that the good public health system in Taiwan has provided a strong support for containment of the Covid-19 epidemic. This study is expected to provide some experience for public health construction of Chinese Mainland to improve Chinese emergency management capacity.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Since the start of 2020, Covid-19 has been wreaking havoc around the world, posing threats to economic growth and people's lives. Through literature research, this study first studied on laws and regulations of public health, and summarized the normalized epidemic control policies of Taiwan. Later, by sorting news and administrative documents on Covid-19 control in Taiwan, this study analyzed features on the system of early warning, risk control, border quarantine inspection, medical work, and security of materials. Research results show that the good public health system in Taiwan has provided a strong support for containment of the Covid-19 epidemic. This study is expected to provide some experience for public health construction of Chinese Mainland to improve Chinese emergency management capacity.",
"fno": "857100a086",
"keywords": [
"Diseases",
"Economic Indicators",
"Emergency Management",
"Government Policies",
"Health And Safety",
"Legislation",
"Public Administration",
"Risk Management",
"Covid 19 Epidemic",
"Economic Growth",
"Normalized Epidemic Control Policies",
"Risk Control System",
"Public Health Construction",
"Public Health Regulation",
"Taiwan",
"Early Warning System",
"Medical Work",
"Public Health Laws",
"Border Quarantine Inspection",
"Chinese Mainland",
"Chinese Emergency Management Capacity",
"Material Security",
"COVID 19",
"Epidemics",
"Solids",
"Regulation",
"Security",
"Public Healthcare",
"Sorting",
"Epidemic",
"Public Health",
"Emergency Management"
],
"authors": [
{
"affiliation": "China Pharmaceutical University,Pharmacy Administration Department,Nanjing,China",
"fullName": "Ziyi Zhang",
"givenName": "Ziyi",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icphds",
"isOpenAccess": true,
"showRecommendedArticles": true,
"showBuyMe": false,
"hasPdf": true,
"pubDate": "2020-11-01T00:00:00",
"pubType": "proceedings",
"pages": "86-89",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-8571-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "857100a081",
"articleId": "1rxhu9O7SP6",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "857100a090",
"articleId": "1rxhpczkLTy",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2021/0126/0/09669328",
"title": "Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669328/1A9VXybnCTu",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icuems/2020/8832/0/09151826",
"title": "Study on Epidemic Prevention and Control Strategy of COVID -19 Based on Personnel Flow Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/icuems/2020/09151826/1lRlPHkznna",
"parentPublication": {
"id": "proceedings/icuems/2020/8832/0",
"title": "2020 International Conference on Urban Engineering and Management Science (ICUEMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cisce/2020/9761/0/976100a143",
"title": "Evaluating the Effectiveness of COVID-19 Prevention and Control Measures Based on SEIR Model",
"doi": null,
"abstractUrl": "/proceedings-article/cisce/2020/976100a143/1oUCUS5E2I0",
"parentPublication": {
"id": "proceedings/cisce/2020/9761/0",
"title": "2020 International Conference on Communications, Information System and Computer Engineering (CISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09312981",
"title": "An Improved SEIR Model for Reconstructing the Dynamic Transmission of COVID-19",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09312981/1qmgfLRlMUE",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a377",
"title": "Research Progress of Chinese Public Health System Reform under COVID-19",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a377/1rxhpKXlTS8",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a180",
"title": "How COVID-19 Affects Mental Health of Wuhan College Students and It’s Countermeasures",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a180/1rxhqKHFTag",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2020/8571/0/857100a116",
"title": "Western perspectives of the COVID-19 in China",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2020/857100a116/1rxhqmRWa0o",
"parentPublication": {
"id": "proceedings/icphds/2020/8571/0",
"title": "2020 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09378472",
"title": "Toward A Multilingual and Multimodal Data Repository for COVID-19 Disinformation",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09378472/1s64iSQht4s",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieit/2021/2563/0/256300a304",
"title": "The Integration of Spirit of Combating the COVID-19 Epidemic into Ideological and Political Education in Universities under the background of Internet",
"doi": null,
"abstractUrl": "/proceedings-article/ieit/2021/256300a304/1wHKuPxFW0M",
"parentPublication": {
"id": "proceedings/ieit/2021/2563/0",
"title": "2021 International Conference on Internet, Education and Information Technology (IEIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icphds/2021/2594/0/259400a101",
"title": "A Data-based Research on China’s National Image in COVID-19 Epidemic Reports Covered by German Mainstream Media",
"doi": null,
"abstractUrl": "/proceedings-article/icphds/2021/259400a101/1ymILVEmVMc",
"parentPublication": {
"id": "proceedings/icphds/2021/2594/0",
"title": "2021 International Conference on Public Health and Data Science (ICPHDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1stvtwjKIQU",
"title": "2020 6th International Conference on Big Data and Information Analytics (BigDIA)",
"acronym": "bigdia",
"groupId": "1830165",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1stvxHw4RVu",
"doi": "10.1109/BigDIA51454.2020.00063",
"title": "Data-driven Network Model of COVID-19 Epidemic in Italy",
"normalizedTitle": "Data-driven Network Model of COVID-19 Epidemic in Italy",
"abstract": "Since first reported publicly in Wuhan, the novel coronavirus pneumonia (COVID-19) has attracted the attention all over the world. As one of the worst-hit places, Italy had registered 1,479,910 tested cases, with 211,938 infected cases, 82,879 recoveries, 29,079 deaths as of 29 June 2020. The Italian authorities have implemented multiple control strategies, such as basic social-distancing measures, early measures (including closing the mall and school), and even nationwide lockdown. Herein, a novel model, named as S2E2IHRD, is presented to reveal the course of epidemic in Italy, where eight groups of the population are considered: S, susceptible cases (unquarantined); Sq, susceptible cases (quarantined); E, exposed cases (infected, undetected); Eq1, exposed cases (infected, quarantined); I, infected cases (confirmed, undetected); H, hospitalized cases; R, recovered cases; D, dead cases. Our model distinguishes the infections based on whether they are quarantined or hospitalized. The essential distinction between quarantined/hospitalized and unquarantined/non-hospitalized cases is that the former are typically isolated who appear to unlikely spread the infection. The implementation of countermeasures has been explained in the model, the short-term simulation results have demonstrated the accuracy of our model by comparing with real data on the COVID-19 epidemic in Italy. We also model various simulation scenarios to illustrate the performance of countermeasures.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Since first reported publicly in Wuhan, the novel coronavirus pneumonia (COVID-19) has attracted the attention all over the world. As one of the worst-hit places, Italy had registered 1,479,910 tested cases, with 211,938 infected cases, 82,879 recoveries, 29,079 deaths as of 29 June 2020. The Italian authorities have implemented multiple control strategies, such as basic social-distancing measures, early measures (including closing the mall and school), and even nationwide lockdown. Herein, a novel model, named as S2E2IHRD, is presented to reveal the course of epidemic in Italy, where eight groups of the population are considered: S, susceptible cases (unquarantined); Sq, susceptible cases (quarantined); E, exposed cases (infected, undetected); Eq1, exposed cases (infected, quarantined); I, infected cases (confirmed, undetected); H, hospitalized cases; R, recovered cases; D, dead cases. Our model distinguishes the infections based on whether they are quarantined or hospitalized. The essential distinction between quarantined/hospitalized and unquarantined/non-hospitalized cases is that the former are typically isolated who appear to unlikely spread the infection. The implementation of countermeasures has been explained in the model, the short-term simulation results have demonstrated the accuracy of our model by comparing with real data on the COVID-19 epidemic in Italy. We also model various simulation scenarios to illustrate the performance of countermeasures.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Since first reported publicly in Wuhan, the novel coronavirus pneumonia (COVID-19) has attracted the attention all over the world. As one of the worst-hit places, Italy had registered 1,479,910 tested cases, with 211,938 infected cases, 82,879 recoveries, 29,079 deaths as of 29 June 2020. The Italian authorities have implemented multiple control strategies, such as basic social-distancing measures, early measures (including closing the mall and school), and even nationwide lockdown. Herein, a novel model, named as S2E2IHRD, is presented to reveal the course of epidemic in Italy, where eight groups of the population are considered: S, susceptible cases (unquarantined); Sq, susceptible cases (quarantined); E, exposed cases (infected, undetected); Eq1, exposed cases (infected, quarantined); I, infected cases (confirmed, undetected); H, hospitalized cases; R, recovered cases; D, dead cases. Our model distinguishes the infections based on whether they are quarantined or hospitalized. The essential distinction between quarantined/hospitalized and unquarantined/non-hospitalized cases is that the former are typically isolated who appear to unlikely spread the infection. The implementation of countermeasures has been explained in the model, the short-term simulation results have demonstrated the accuracy of our model by comparing with real data on the COVID-19 epidemic in Italy. We also model various simulation scenarios to illustrate the performance of countermeasures.",
"fno": "223200a354",
"keywords": [
"Diseases",
"Epidemics",
"Hospitals",
"Modelling",
"Simulation",
"Exposed Cases",
"Hospitalized Cases",
"Recovered Cases",
"Dead Cases",
"COVID 19 Epidemic",
"Coronavirus Pneumonia",
"Multiple Control Strategies",
"Social Distancing",
"Susceptible Cases",
"Data Driven Network Model",
"Italy",
"S 2 E 2 IHRD Model",
"Infected Cases",
"Wuhan",
"Short Term Simulation",
"Predictive Mathematical Model",
"SIR Model",
"Epidemic Model",
"Quarantined Cases",
"COVID 19",
"Epidemics",
"Simulation",
"Sociology",
"Predictive Models",
"Data Models",
"Statistics",
"COVID 19",
"Data Driven",
"Epidemic Evolution",
"Network Model",
"Prediction"
],
"authors": [
{
"affiliation": "College of Systems Engineering, National University of Defense Technology,Changsha,China",
"fullName": "Yuming Huang",
"givenName": "Yuming",
"surname": "Huang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Systems Engineering, National University of Defense Technology,Changsha,China",
"fullName": "Bingfeng Ge",
"givenName": "Bingfeng",
"surname": "Ge",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Systems Engineering, National University of Defense Technology,Changsha,China",
"fullName": "Bin Zhao",
"givenName": "Bin",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Systems Engineering, National University of Defense Technology,Changsha,China",
"fullName": "Yingying Gao",
"givenName": "Yingying",
"surname": "Gao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Systems Engineering, National University of Defense Technology,Changsha,China",
"fullName": "Zeqiang Hou",
"givenName": "Zeqiang",
"surname": "Hou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Systems Engineering, National University of Defense Technology,Changsha,China",
"fullName": "Kewei Yang",
"givenName": "Kewei",
"surname": "Yang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bigdia",
"isOpenAccess": true,
"showRecommendedArticles": true,
"showBuyMe": false,
"hasPdf": true,
"pubDate": "2020-12-01T00:00:00",
"pubType": "proceedings",
"pages": "354-360",
"year": "2020",
"issn": null,
"isbn": "978-1-6654-2232-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "223200a347",
"articleId": "1stvtIpplni",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "223200a361",
"articleId": "1stvwBa9HWg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tb/5555/01/09955483",
"title": "Quantifying the Effect of Quarantine Control and Optimizing its Cost in COVID-19 Pandemic",
"doi": null,
"abstractUrl": "/journal/tb/5555/01/09955483/1Ip3jXUosgg",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icftic/2022/2195/0/10075276",
"title": "Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode",
"doi": null,
"abstractUrl": "/proceedings-article/icftic/2022/10075276/1LRl7WPLY5y",
"parentPublication": {
"id": "proceedings/icftic/2022/2195/0",
"title": "2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbms/2020/9429/0/942900a277",
"title": "Time-Window SIQR Analysis of COVID-19 Outbreak and Containment Measures in Italy",
"doi": null,
"abstractUrl": "/proceedings-article/cbms/2020/942900a277/1mLMiBAUMg0",
"parentPublication": {
"id": "proceedings/cbms/2020/9429/0",
"title": "2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313396",
"title": "Covid-19 Signal Analysis: Effect of Lockdown and Unlockdowns on Normalized Entropy in Italy",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313396/1qmg0c0Dke4",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313291",
"title": "A method to assess COVID-19 infected numbers in Italy during peak pandemic period",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313291/1qmgbiuHjI4",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaice/2020/9146/0/914600a383",
"title": "COVID-19 Spreading Prediction with Enhanced SEIR Model",
"doi": null,
"abstractUrl": "/proceedings-article/icaice/2020/914600a383/1rCgb72Z6s8",
"parentPublication": {
"id": "proceedings/icaice/2020/9146/0",
"title": "2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dfhmc/2020/1518/0/151800a293",
"title": "Covid 19, Re-Infection and Its Potential Impact on Economy: A Policy-Based Analysis with Mathemetical Modelling (SIR)",
"doi": null,
"abstractUrl": "/proceedings-article/dfhmc/2020/151800a293/1tcjTbkkld6",
"parentPublication": {
"id": "proceedings/dfhmc/2020/1518/0",
"title": "2020 16th Dahe Fortune China Forum and Chinese High-educational Management Annual Academic Conference (DFHMC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cds/2021/0428/0/042800a064",
"title": "Data-driven COVID-19 growth prediction",
"doi": null,
"abstractUrl": "/proceedings-article/cds/2021/042800a064/1uZxwpLhI2c",
"parentPublication": {
"id": "proceedings/cds/2021/0428/0",
"title": "2021 2nd International Conference on Computing and Data Science (CDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2021/2463/0/246300b711",
"title": "COVID-19 SIHR Modeling and Dynamic Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2021/246300b711/1wLcIggw3Ru",
"parentPublication": {
"id": "proceedings/compsac/2021/2463/0",
"title": "2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaa/2021/3730/0/373000a565",
"title": "NIPGM(1,1,t.<sup>\\alpha</sup>) Grey Model of COVID-19 Population Prediction Based on Slime Mold Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icaa/2021/373000a565/1zL1HbCPip2",
"parentPublication": {
"id": "proceedings/icaa/2021/3730/0",
"title": "2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNBUAvV5",
"title": "2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW)",
"acronym": "icdew",
"groupId": "1001384",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvEyR7r",
"doi": "10.1109/ICDEW.2014.6818305",
"title": "Scholarly big data information extraction and integration in the CiteSeerχ digital library",
"normalizedTitle": "Scholarly big data information extraction and integration in the CiteSeerχ digital library",
"abstract": "CiteSeerχ is a digital library that contains approximately 3.5 million scholarly documents and receives between 2 and 4 million requests per day. In addition to making documents available via a public Website, the data is also used to facilitate research in areas like citation analysis, co-author network analysis, scalability evaluation and information extraction. The papers in CiteSeerχ are gathered from the Web by means of continuous automatic focused crawling and go through a series of automatic processing steps as part of the ingestion process. Given the size of the collection, the fact that it is constantly expanding, and the multiple ways in which it is used both by the public to access scholarly documents and for research, there are several big data challenges. In this paper, we provide a case study description of how we address these challenges when it comes to information extraction, data integration and entity linking in CiteSeerχ. We describe how we: aggregate data from multiple sources on the Web; store and manage data; process data as part of an automatic ingestion pipeline that includes automatic metadata and information extraction; perform document and citation clustering; perform entity linking and name disambiguation; and make our data and source code available to enable research and collaboration.",
"abstracts": [
{
"abstractType": "Regular",
"content": "CiteSeerχ is a digital library that contains approximately 3.5 million scholarly documents and receives between 2 and 4 million requests per day. In addition to making documents available via a public Website, the data is also used to facilitate research in areas like citation analysis, co-author network analysis, scalability evaluation and information extraction. The papers in CiteSeerχ are gathered from the Web by means of continuous automatic focused crawling and go through a series of automatic processing steps as part of the ingestion process. Given the size of the collection, the fact that it is constantly expanding, and the multiple ways in which it is used both by the public to access scholarly documents and for research, there are several big data challenges. In this paper, we provide a case study description of how we address these challenges when it comes to information extraction, data integration and entity linking in CiteSeerχ. We describe how we: aggregate data from multiple sources on the Web; store and manage data; process data as part of an automatic ingestion pipeline that includes automatic metadata and information extraction; perform document and citation clustering; perform entity linking and name disambiguation; and make our data and source code available to enable research and collaboration.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "CiteSeerχ is a digital library that contains approximately 3.5 million scholarly documents and receives between 2 and 4 million requests per day. In addition to making documents available via a public Website, the data is also used to facilitate research in areas like citation analysis, co-author network analysis, scalability evaluation and information extraction. The papers in CiteSeerχ are gathered from the Web by means of continuous automatic focused crawling and go through a series of automatic processing steps as part of the ingestion process. Given the size of the collection, the fact that it is constantly expanding, and the multiple ways in which it is used both by the public to access scholarly documents and for research, there are several big data challenges. In this paper, we provide a case study description of how we address these challenges when it comes to information extraction, data integration and entity linking in CiteSeerχ. We describe how we: aggregate data from multiple sources on the Web; store and manage data; process data as part of an automatic ingestion pipeline that includes automatic metadata and information extraction; perform document and citation clustering; perform entity linking and name disambiguation; and make our data and source code available to enable research and collaboration.",
"fno": "06818305",
"keywords": [],
"authors": [
{
"affiliation": "Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA",
"fullName": "Kyle Williams",
"givenName": "Kyle",
"surname": "Williams",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA",
"fullName": "Jian Wu",
"givenName": "Jian",
"surname": "Wu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA",
"fullName": "Sagnik Ray Choudhury",
"givenName": "Sagnik Ray",
"surname": "Choudhury",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802, USA",
"fullName": "Madian Khabsa",
"givenName": "Madian",
"surname": "Khabsa",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802, USA",
"fullName": "C. Lee Giles",
"givenName": "C. Lee",
"surname": "Giles",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icdew",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-03-01T00:00:00",
"pubType": "proceedings",
"pages": "68-73",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-3481-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "06818304",
"articleId": "12OmNzlly2X",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06818306",
"articleId": "12OmNvRU0rP",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/jcdl/2016/4229/0/07559627",
"title": "Information extraction for scholarly digital libraries",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2016/07559627/12OmNAQanwy",
"parentPublication": {
"id": "proceedings/jcdl/2016/4229/0",
"title": "2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icws/2014/5054/0/5054a105",
"title": "A Web Service for Scholarly Big Data Information Extraction",
"doi": null,
"abstractUrl": "/proceedings-article/icws/2014/5054a105/12OmNAXxX1r",
"parentPublication": {
"id": "proceedings/icws/2014/5054/0",
"title": "2014 IEEE International Conference on Web Services (ICWS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2013/4999/0/06628716",
"title": "Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2013/06628716/12OmNyKa659",
"parentPublication": {
"id": "proceedings/icdar/2013/4999/0",
"title": "2013 12th International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2014/5569/0/06970157",
"title": "Towards building a scholarly big data platform: Challenges, lessons and opportunities",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2014/06970157/12OmNyS6RGa",
"parentPublication": {
"id": "proceedings/jcdl/2014/5569/0",
"title": "2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2003/02/x2058",
"title": "Citation-Based Retrieval for Scholarly Publications",
"doi": null,
"abstractUrl": "/magazine/ex/2003/02/x2058/13rRUEgarxc",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2016/01/07448389",
"title": "AlgorithmSeer: A System for Extracting and Searching for Algorithms in Scholarly Big Data",
"doi": null,
"abstractUrl": "/journal/bd/2016/01/07448389/13rRUx0Pquv",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2018/5520/0/552000a953",
"title": "Query Independent Scholarly Article Ranking",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2018/552000a953/14Fq0Y7YFT0",
"parentPublication": {
"id": "proceedings/icde/2018/5520/0",
"title": "2018 IEEE 34th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2021/3902/0/09671612",
"title": "Building an Accessible, Usable, Scalable, and Sustainable Service for Scholarly Big Data",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2021/09671612/1A8jcgOZxQI",
"parentPublication": {
"id": "proceedings/big-data/2021/3902/0",
"title": "2021 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ec/2021/04/08903467",
"title": "DICO: A Graph-DB Framework for Community Detection on Big Scholarly Data",
"doi": null,
"abstractUrl": "/journal/ec/2021/04/08903467/1fapHWBhQuA",
"parentPublication": {
"id": "trans/ec",
"title": "IEEE Transactions on Emerging Topics in Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ec/2021/01/09372350",
"title": "Guest Editorial: Scholarly Big Data",
"doi": null,
"abstractUrl": "/journal/ec/2021/01/09372350/1rNPLRE82By",
"parentPublication": {
"id": "trans/ec",
"title": "IEEE Transactions on Emerging Topics in Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNxdm4Jm",
"title": "2015 IEEE 11th International Conference on e-Science (e-Science)",
"acronym": "e-science",
"groupId": "1001511",
"volume": "0",
"displayVolume": "0",
"year": "2015",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxiKscb",
"doi": "10.1109/eScience.2015.34",
"title": "Interactive Analytic Systems for Understanding the Scholarly Impact of Large-Scale E-science Cyberenvironments",
"normalizedTitle": "Interactive Analytic Systems for Understanding the Scholarly Impact of Large-Scale E-science Cyberenvironments",
"abstract": "Cyber-environments have become increasingly popular in disseminating scientific and educational resources. One of the primary methodologies for evaluating the scholarly impact of cyber-environment is through bibliometric and scientometrics analyses of publication and citation data. However, it is often difficult to create a workflow and software environment that can address this problem in serious ways. This article presents the software architecture for creating a web-based interface for managing citations and demonstrating visually the scholarly impact of a cyber-environment - nanoHUB.org, which has served over 310,000 users worldwide in just the last 12 months.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Cyber-environments have become increasingly popular in disseminating scientific and educational resources. One of the primary methodologies for evaluating the scholarly impact of cyber-environment is through bibliometric and scientometrics analyses of publication and citation data. However, it is often difficult to create a workflow and software environment that can address this problem in serious ways. This article presents the software architecture for creating a web-based interface for managing citations and demonstrating visually the scholarly impact of a cyber-environment - nanoHUB.org, which has served over 310,000 users worldwide in just the last 12 months.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Cyber-environments have become increasingly popular in disseminating scientific and educational resources. One of the primary methodologies for evaluating the scholarly impact of cyber-environment is through bibliometric and scientometrics analyses of publication and citation data. However, it is often difficult to create a workflow and software environment that can address this problem in serious ways. This article presents the software architecture for creating a web-based interface for managing citations and demonstrating visually the scholarly impact of a cyber-environment - nanoHUB.org, which has served over 310,000 users worldwide in just the last 12 months.",
"fno": "9325a288",
"keywords": [
"Metadata",
"Citation Analysis",
"Data Visualization",
"Databases",
"Visual Analytics",
"Software",
"Research Evaluation",
"Bibliometrics",
"Cyber Environment",
"Visual Analytics",
"Big Data",
"Data Mining",
"Studying Impact"
],
"authors": [
{
"affiliation": null,
"fullName": "Krishna Madhavan",
"givenName": "Krishna",
"surname": "Madhavan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Daniel F. Mejia",
"givenName": "Daniel F.",
"surname": "Mejia",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Hanjun Xian",
"givenName": "Hanjun",
"surname": "Xian",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Lynn K. Zentner",
"givenName": "Lynn K.",
"surname": "Zentner",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Victoria A. Farnsworth",
"givenName": "Victoria A.",
"surname": "Farnsworth",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Swaroop Samek",
"givenName": "Swaroop",
"surname": "Samek",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Gerhard Klimeck",
"givenName": "Gerhard",
"surname": "Klimeck",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "e-science",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2015-08-01T00:00:00",
"pubType": "proceedings",
"pages": "288-291",
"year": "2015",
"issn": null,
"isbn": "978-1-4673-9325-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "9325a284",
"articleId": "12OmNz4Bdg4",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "9325a292",
"articleId": "12OmNxGj9Rq",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/hicss/2013/4892/0/4892a126",
"title": "A Qualitative Methodology for the Design of Visual Analytic Tools for Emergency Operation Centers",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2013/4892a126/12OmNCcbEjG",
"parentPublication": {
"id": "proceedings/hicss/2013/4892/0",
"title": "2013 46th Hawaii International Conference on System Sciences",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2015/9325/0/9325a401",
"title": "Globus Data Publication as a Service: Lowering Barriers to Reproducible Science",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2015/9325a401/12OmNrJiCPr",
"parentPublication": {
"id": "proceedings/e-science/2015/9325/0",
"title": "2015 IEEE 11th International Conference on e-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ldav/2011/0155/0/06092312",
"title": "Atypical behavior identification in large-scale network traffic",
"doi": null,
"abstractUrl": "/proceedings-article/ldav/2011/06092312/12OmNzGlRzn",
"parentPublication": {
"id": "proceedings/ldav/2011/0155/0",
"title": "IEEE Symposium on Large Data Analysis and Visualization (LDAV 2011)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2016/01/07194834",
"title": "SensePath: Understanding the Sensemaking Process Through Analytic Provenance",
"doi": null,
"abstractUrl": "/journal/tg/2016/01/07194834/13rRUEgarnM",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icws/2018/7247/0/724701a265",
"title": "A Web Service for Author Name Disambiguation in Scholarly Databases",
"doi": null,
"abstractUrl": "/proceedings-article/icws/2018/724701a265/13rRUxN5euZ",
"parentPublication": {
"id": "proceedings/icws/2018/7247/0",
"title": "2018 IEEE International Conference on Web Services (ICWS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ec/2021/01/08384017",
"title": "FacetsBase: A Key-Value Store Optimized for Querying on Scholarly Data",
"doi": null,
"abstractUrl": "/journal/ec/2021/01/08384017/13rRUxYIMQL",
"parentPublication": {
"id": "trans/ec",
"title": "IEEE Transactions on Emerging Topics in Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09747941",
"title": "SD^2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09747941/1CdB6lneKkg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cs/2020/01/08781923",
"title": "Jupyter Notebooks as Discovery Mechanisms for Open Science: Citation Practices in the Astronomy Community",
"doi": null,
"abstractUrl": "/magazine/cs/2020/01/08781923/1c5tgvY9oHu",
"parentPublication": {
"id": "mags/cs",
"title": "Computing in Science & Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2019/2838/0/283800a255",
"title": "CHRAVAT - Chronology Awareness Visual Analytic Tool",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2019/283800a255/1cMF9pFwpig",
"parentPublication": {
"id": "proceedings/iv/2019/2838/0",
"title": "2019 23rd International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/01/09552870",
"title": "Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers",
"doi": null,
"abstractUrl": "/journal/tg/2022/01/09552870/1xic90zZWDu",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNBpVPR3",
"title": "Proceedings of International Conference on Expert Systems for Development",
"acronym": "icesd",
"groupId": "1001973",
"volume": "0",
"displayVolume": "0",
"year": "1994",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNALUozj",
"doi": "10.1109/ICESD.1994.302277",
"title": "A hybrid approach to path planning in autonomous agents",
"normalizedTitle": "A hybrid approach to path planning in autonomous agents",
"abstract": "The authors focus on the integration of symbolic and sub-symbolic knowledge for the execution of path-planning tasks in autonomous agents. Environmental knowledge is represented through a multilayered architecture whose different abstraction levels are identified by means of meta-knowledge for classification and clustering of distinctive places. The path-planning problem considered consists of determining the cheapest path for visiting a set of resources in the environment, each resource being expressed as either a cluster or a category of clusters at any abstraction level. Time windows and precedence constraints between resources are taken into account. The algorithm proposed finds a sub-optimal solution to this problem by decomposing it at the different abstraction levels through a divide-et-impera technique.<>",
"abstracts": [
{
"abstractType": "Regular",
"content": "The authors focus on the integration of symbolic and sub-symbolic knowledge for the execution of path-planning tasks in autonomous agents. Environmental knowledge is represented through a multilayered architecture whose different abstraction levels are identified by means of meta-knowledge for classification and clustering of distinctive places. The path-planning problem considered consists of determining the cheapest path for visiting a set of resources in the environment, each resource being expressed as either a cluster or a category of clusters at any abstraction level. Time windows and precedence constraints between resources are taken into account. The algorithm proposed finds a sub-optimal solution to this problem by decomposing it at the different abstraction levels through a divide-et-impera technique.<>",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The authors focus on the integration of symbolic and sub-symbolic knowledge for the execution of path-planning tasks in autonomous agents. Environmental knowledge is represented through a multilayered architecture whose different abstraction levels are identified by means of meta-knowledge for classification and clustering of distinctive places. The path-planning problem considered consists of determining the cheapest path for visiting a set of resources in the environment, each resource being expressed as either a cluster or a category of clusters at any abstraction level. Time windows and precedence constraints between resources are taken into account. The algorithm proposed finds a sub-optimal solution to this problem by decomposing it at the different abstraction levels through a divide-et-impera technique.",
"fno": "00302277",
"keywords": [
"Mobile Robots",
"Path Planning",
"Computerised Navigation",
"Intelligent Control",
"Resource Allocation",
"Data Structures",
"Knowledge Representation",
"Hybrid Approach",
"Path Planning",
"Autonomous Agents",
"Sub Symbolic Knowledge",
"Environmental Knowledge",
"Multilayered Architecture",
"Abstraction Levels",
"Meta Knowledge",
"Cheapest Path",
"Time Windows",
"Precedence Constraints",
"Sub Optimal Solution",
"Divide Et Impera Technique",
"Mobile Robots",
"Autonomous Navigation",
"Path Planning",
"Autonomous Agents",
"Navigation",
"Knowledge Representation",
"Clustering Algorithms",
"Hospitals",
"Sensor Phenomena And Characterization",
"Mobile Robots",
"Production Facilities",
"Solid Modeling"
],
"authors": [
{
"affiliation": "Dipartimento di Elettronica, Inf. e Sistemistica, Bologna Univ., Italy",
"fullName": "D. Maio",
"givenName": "D.",
"surname": "Maio",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dipartimento di Elettronica, Inf. e Sistemistica, Bologna Univ., Italy",
"fullName": "S. Rizzi",
"givenName": "S.",
"surname": "Rizzi",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icesd",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "1994-01-01T00:00:00",
"pubType": "proceedings",
"pages": "222,223,224,225,226,227",
"year": "1994",
"issn": null,
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "00302276",
"articleId": "12OmNyKJipf",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "00302278",
"articleId": "12OmNxXl5Bb",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/eurobot/1997/8174/0/81740045",
"title": "An architecture for autonomous agents integrating symbolic and behavioral processing",
"doi": null,
"abstractUrl": "/proceedings-article/eurobot/1997/81740045/12OmNAkWvzv",
"parentPublication": {
"id": "proceedings/eurobot/1997/8174/0",
"title": "Advanced Mobile Robots, Euromicro Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/1996/7686/0/76860354",
"title": "Global Path Planning for Autonomous Qualitative Navigation",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/1996/76860354/12OmNrJRPjI",
"parentPublication": {
"id": "proceedings/ictai/1996/7686/0",
"title": "Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wcecs/2008/3555/0/3555a182",
"title": "Autonomous Mobile Robot: Path Planning Using Backward Chaining",
"doi": null,
"abstractUrl": "/proceedings-article/wcecs/2008/3555a182/12OmNzdoMn2",
"parentPublication": {
"id": "proceedings/wcecs/2008/3555/0",
"title": "World Congress on Engineering and Computer Science, Advances in Electrical and Electronics Engineering - IAENG Special Edition of the",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2017/3876/0/387601a951",
"title": "Robust Inverse Planning Approaches for Policy Estimation of Semi-autonomous Agents",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2017/387601a951/12OmNzvz6BZ",
"parentPublication": {
"id": "proceedings/ictai/2017/3876/0",
"title": "2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/1996/11/i1080",
"title": "Dynamic Clustering of Maps in Autonomous Agents",
"doi": null,
"abstractUrl": "/journal/tp/1996/11/i1080/13rRUxly8Yt",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigcomp/2019/7789/0/08679340",
"title": "Open-Source Tool of Vector Map for Path Planning in Autoware Autonomous Driving Software",
"doi": null,
"abstractUrl": "/proceedings-article/bigcomp/2019/08679340/18XkinYANZS",
"parentPublication": {
"id": "proceedings/bigcomp/2019/7789/0",
"title": "2019 IEEE International Conference on Big Data and Smart Computing (BigComp)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/arace/2022/5153/0/515300a193",
"title": "UAV path planning based on the improved PPO algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/arace/2022/515300a193/1Ip7KIcSsco",
"parentPublication": {
"id": "proceedings/arace/2022/5153/0",
"title": "2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/percom-workshops/2019/9151/0/08730688",
"title": "Scene Context-aware Rapidly-exploring Random Trees for Global Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/percom-workshops/2019/08730688/1aDSNMihE6k",
"parentPublication": {
"id": "proceedings/percom-workshops/2019/9151/0",
"title": "2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/sp/2020/03/09091352",
"title": "Doers, Not Watchers: Intelligent Autonomous Agents Are a Path to Cyber Resilience",
"doi": null,
"abstractUrl": "/magazine/sp/2020/03/09091352/1jK9UcLVumc",
"parentPublication": {
"id": "mags/sp",
"title": "IEEE Security & Privacy",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acsos/2020/7277/0/09196336",
"title": "Hybrid Planning Using Learning and Model Checking for Autonomous Systems",
"doi": null,
"abstractUrl": "/proceedings-article/acsos/2020/09196336/1n913IrElHi",
"parentPublication": {
"id": "proceedings/acsos/2020/7277/0",
"title": "2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNyRg4mf",
"title": "2014 International Conference on Virtual Reality and Visualization (ICVRV)",
"acronym": "icvrv",
"groupId": "1800579",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNANTAzk",
"doi": "10.1109/ICVRV.2014.76",
"title": "A Multi-layer Approach for Interactive Path Planning Control in Virtual Reality Simulation",
"normalizedTitle": "A Multi-layer Approach for Interactive Path Planning Control in Virtual Reality Simulation",
"abstract": "His work considers path-planning processes for manipulation tasks such as assembly, maintenance or disassembly in a Virtual Reality (VR) context. The approach consists in providing a collaborative system associating a user immersed in VR and an automatic path planning process. It is based on an novel environment model containing semantic, topological and geometric information and an original planning process split in two phases: coarse and fine planning. The automatic planner suggests a path to the user and guides him trough a hap tic device. The user can escape from the proposed solution if he wants to explore a possible better way. In this case, the interactive system detects the users intention and computes in real-time a new path taking the users intent into account. Experiments illustrate the different aspects of the proposed approach: multi-representation of the environment, path planning process, users intent prediction and control sharing.",
"abstracts": [
{
"abstractType": "Regular",
"content": "His work considers path-planning processes for manipulation tasks such as assembly, maintenance or disassembly in a Virtual Reality (VR) context. The approach consists in providing a collaborative system associating a user immersed in VR and an automatic path planning process. It is based on an novel environment model containing semantic, topological and geometric information and an original planning process split in two phases: coarse and fine planning. The automatic planner suggests a path to the user and guides him trough a hap tic device. The user can escape from the proposed solution if he wants to explore a possible better way. In this case, the interactive system detects the users intention and computes in real-time a new path taking the users intent into account. Experiments illustrate the different aspects of the proposed approach: multi-representation of the environment, path planning process, users intent prediction and control sharing.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "His work considers path-planning processes for manipulation tasks such as assembly, maintenance or disassembly in a Virtual Reality (VR) context. The approach consists in providing a collaborative system associating a user immersed in VR and an automatic path planning process. It is based on an novel environment model containing semantic, topological and geometric information and an original planning process split in two phases: coarse and fine planning. The automatic planner suggests a path to the user and guides him trough a hap tic device. The user can escape from the proposed solution if he wants to explore a possible better way. In this case, the interactive system detects the users intention and computes in real-time a new path taking the users intent into account. Experiments illustrate the different aspects of the proposed approach: multi-representation of the environment, path planning process, users intent prediction and control sharing.",
"fno": "6854a296",
"keywords": [
"Planning",
"Path Planning",
"Semantics",
"Solid Modeling",
"Computational Modeling",
"Robots",
"Shape",
"Multi Layer Architecture",
"Virtual Reality",
"Interactive Path Planning",
"Control Sharing"
],
"authors": [
{
"affiliation": null,
"fullName": "Simon Cailhol",
"givenName": "Simon",
"surname": "Cailhol",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Philippe Fillatreau",
"givenName": "Philippe",
"surname": "Fillatreau",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jean-Yves Fourquet",
"givenName": "Jean-Yves",
"surname": "Fourquet",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yingshen Zhao",
"givenName": "Yingshen",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icvrv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-08-01T00:00:00",
"pubType": "proceedings",
"pages": "296-301",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-6854-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "6854a290",
"articleId": "12OmNAnMuwS",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "6854a302",
"articleId": "12OmNwE9OUY",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ecmsm/2013/6298/0/06648954",
"title": "Interactive multimodal path planning in immersion",
"doi": null,
"abstractUrl": "/proceedings-article/ecmsm/2013/06648954/12OmNBU1jIG",
"parentPublication": {
"id": "proceedings/ecmsm/2013/6298/0",
"title": "2013 IEEE 11th International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1989/1938/0/00100007",
"title": "Global path planning using artificial potential fields",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1989/00100007/12OmNqyDjq1",
"parentPublication": {
"id": "proceedings/robot/1989/1938/0",
"title": "1989 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isatp/2003/7770/0/01217219",
"title": "Path planning for flexible components using a virtual reality environment",
"doi": null,
"abstractUrl": "/proceedings-article/isatp/2003/01217219/12OmNvxbhKc",
"parentPublication": {
"id": "proceedings/isatp/2003/7770/0",
"title": "ISATP'03: 5th IEEE International Symposium on Assembly and Task Planning",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2015/1727/0/07223344",
"title": "A multi-layer approach of interactive path planning for assisted manipulation in virtual reality",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2015/07223344/12OmNy2agQe",
"parentPublication": {
"id": "proceedings/vr/2015/1727/0",
"title": "2015 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/crv/2012/4683/0/4683a417",
"title": "Socially-Driven Collective Path Planning for Robot Missions",
"doi": null,
"abstractUrl": "/proceedings-article/crv/2012/4683a417/12OmNyaXPQG",
"parentPublication": {
"id": "proceedings/crv/2012/4683/0",
"title": "2012 Ninth Conference on Computer and Robot Vision",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/crc/2019/4620/0/462000a145",
"title": "A Literature Review on Path Planning of Polyhedrons with Rolling Contact",
"doi": null,
"abstractUrl": "/proceedings-article/crc/2019/462000a145/1iTuKBvPEd2",
"parentPublication": {
"id": "proceedings/crc/2019/4620/0",
"title": "2019 4th International Conference on Control, Robotics and Cybernetics (CRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icci*cc/2019/1419/0/09146050",
"title": "Dynamic Path Optimization for Robot Route Planning",
"doi": null,
"abstractUrl": "/proceedings-article/icci*cc/2019/09146050/1lFJ9Nm9CQo",
"parentPublication": {
"id": "proceedings/icci*cc/2019/1419/0",
"title": "2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cibda/2020/9837/0/983700a408",
"title": "Design and Application of 3D Path Planning Model of Agricultural Automatic Robot",
"doi": null,
"abstractUrl": "/proceedings-article/cibda/2020/983700a408/1lO1MrMRBgk",
"parentPublication": {
"id": "proceedings/cibda/2020/9837/0",
"title": "2020 International Conference on Computer Information and Big Data Applications (CIBDA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvris/2020/9636/0/963600a946",
"title": "Path Planning Based on Improved Ant Colony Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icvris/2020/963600a946/1x4Z4G0n9Cw",
"parentPublication": {
"id": "proceedings/icvris/2020/9636/0",
"title": "2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ai/2022/03/09576645",
"title": "Robot Path Planning via Neural-Network-Driven Prediction",
"doi": null,
"abstractUrl": "/journal/ai/2022/03/09576645/1xIKwJ9uCqs",
"parentPublication": {
"id": "trans/ai",
"title": "IEEE Transactions on Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNviZlH1",
"title": "2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"acronym": "sitis",
"groupId": "1002425",
"volume": "0",
"displayVolume": "0",
"year": "2013",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBNM92n",
"doi": "10.1109/SITIS.2013.118",
"title": "Global Path Planning for Autonomous Mobile Robot Using Genetic Algorithm",
"normalizedTitle": "Global Path Planning for Autonomous Mobile Robot Using Genetic Algorithm",
"abstract": "Mobile robots work in different kinds of environment, and it is necessary for them to move and maneuver in places with objects and obstacles. In order to navigate the robot in a collision free path, path planning algorithms have been presented. The main goal of path planning is to determine the optimal possible path between the initial point and the defined goal position in the minimal possible time. In this work, a path planning method by utilizing genetic algorithm (GA) is presented. The optimized path in terms of length and cost is generated by GA optimization. The proposed method is a global path planning method with hexagonal grid map modelling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software. The simulation results are presented and analyzed.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Mobile robots work in different kinds of environment, and it is necessary for them to move and maneuver in places with objects and obstacles. In order to navigate the robot in a collision free path, path planning algorithms have been presented. The main goal of path planning is to determine the optimal possible path between the initial point and the defined goal position in the minimal possible time. In this work, a path planning method by utilizing genetic algorithm (GA) is presented. The optimized path in terms of length and cost is generated by GA optimization. The proposed method is a global path planning method with hexagonal grid map modelling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software. The simulation results are presented and analyzed.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Mobile robots work in different kinds of environment, and it is necessary for them to move and maneuver in places with objects and obstacles. In order to navigate the robot in a collision free path, path planning algorithms have been presented. The main goal of path planning is to determine the optimal possible path between the initial point and the defined goal position in the minimal possible time. In this work, a path planning method by utilizing genetic algorithm (GA) is presented. The optimized path in terms of length and cost is generated by GA optimization. The proposed method is a global path planning method with hexagonal grid map modelling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software. The simulation results are presented and analyzed.",
"fno": "3211a726",
"keywords": [
"Path Planning",
"Genetic Algorithms",
"Mobile Robots",
"Robot Kinematics",
"Navigation",
"Sociology",
"Optimization",
"Autonomous Mobile Robot",
"Path Planning",
"Genetic Algorithm"
],
"authors": [
{
"affiliation": null,
"fullName": "Masoud Samadi",
"givenName": "Masoud",
"surname": "Samadi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Mohd Fauzi Othman",
"givenName": "Mohd Fauzi",
"surname": "Othman",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "sitis",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2013-12-01T00:00:00",
"pubType": "proceedings",
"pages": "726-730",
"year": "2013",
"issn": null,
"isbn": "978-1-4799-3211-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "3211a720",
"articleId": "12OmNz61dvc",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "3211a731",
"articleId": "12OmNrkBwHI",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iihmsp/2006/2745/0/04041704",
"title": "Nonholonomic Motion Planning of Mobile Robot with Ameliorated Genetic Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/iihmsp/2006/04041704/12OmNAlNiDb",
"parentPublication": {
"id": "proceedings/iihmsp/2006/2745/0",
"title": "2006 International Conference on Intelligent Information Hiding and Multimedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icnc/2007/2875/2/28750774",
"title": "Niche Genetic Algorithm for Robot Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/icnc/2007/28750774/12OmNBOCWk0",
"parentPublication": {
"id": "proceedings/icnc/2007/2875/2",
"title": "Third International Conference on Natural Computation (ICNC 2007)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icci-cc/2012/2795/0/06311139",
"title": "Path planning for unmanned aerial vehicle based on genetic algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icci-cc/2012/06311139/12OmNCdk2QN",
"parentPublication": {
"id": "proceedings/icci-cc/2012/2795/0",
"title": "2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icgec/2010/4281/0/4281a194",
"title": "Efficient Path Planning Method Based on Genetic Algorithm Combining Path Network",
"doi": null,
"abstractUrl": "/proceedings-article/icgec/2010/4281a194/12OmNrAv3YX",
"parentPublication": {
"id": "proceedings/icgec/2010/4281/0",
"title": "Genetic and Evolutionary Computing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sbrlarsrobocontrol/2014/6711/0/07024268",
"title": "A Hybrid Approach for Path Planning and Execution for Autonomous Mobile Robots",
"doi": null,
"abstractUrl": "/proceedings-article/sbrlarsrobocontrol/2014/07024268/12OmNvTTc9D",
"parentPublication": {
"id": "proceedings/sbrlarsrobocontrol/2014/6711/0",
"title": "2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol (SBR LARS Robocontrol)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cira/1997/8138/0/81380138",
"title": "Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot",
"doi": null,
"abstractUrl": "/proceedings-article/cira/1997/81380138/12OmNwbLVnw",
"parentPublication": {
"id": "proceedings/cira/1997/8138/0",
"title": "Computational Intelligence in Robotics and Automation, IEEE International Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icgec/2010/4281/0/4281a206",
"title": "Quantum Genetic Algorithm for Mobile Robot Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/icgec/2010/4281a206/12OmNwt5snl",
"parentPublication": {
"id": "proceedings/icgec/2010/4281/0",
"title": "Genetic and Evolutionary Computing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icinis/2009/3852/0/3852a278",
"title": "Path Planning for Mobile Robot Based on Rough Set Genetic Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icinis/2009/3852a278/12OmNwwuE48",
"parentPublication": {
"id": "proceedings/icinis/2009/3852/0",
"title": "Intelligent Networks and Intelligent Systems, International Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icnc/2008/3304/4/3304d409",
"title": "Path Planning for Mobile Robot Based on Chaos Genetic Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icnc/2008/3304d409/12OmNzkuKFf",
"parentPublication": {
"id": "proceedings/icnc/2008/3304/4",
"title": "2008 Fourth International Conference on Natural Computation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cyberc/2018/0974/0/097400a417",
"title": "Ground Robot Path Planning Based on Simulated Annealing Genetic Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/cyberc/2018/097400a417/17QjJbTpbZd",
"parentPublication": {
"id": "proceedings/cyberc/2018/0974/0",
"title": "2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNyoiYVr",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBsLPi6",
"doi": "10.1109/CVPR.2017.769",
"title": "Cognitive Mapping and Planning for Visual Navigation",
"normalizedTitle": "Cognitive Mapping and Planning for Visual Navigation",
"abstract": "We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the planner, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. Our experiments demonstrate that CMP outperforms both reactive strategies and standard memory-based architectures and performs well in novel environments. Furthermore, we show that CMP can also achieve semantically specified goals, such as \"go to a chair\".",
"abstracts": [
{
"abstractType": "Regular",
"content": "We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the planner, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. Our experiments demonstrate that CMP outperforms both reactive strategies and standard memory-based architectures and performs well in novel environments. Furthermore, we show that CMP can also achieve semantically specified goals, such as \"go to a chair\".",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the planner, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. Our experiments demonstrate that CMP outperforms both reactive strategies and standard memory-based architectures and performs well in novel environments. Furthermore, we show that CMP can also achieve semantically specified goals, such as \"go to a chair\".",
"fno": "0457h272",
"keywords": [
"Belief Networks",
"Cognitive Systems",
"Neural Net Architecture",
"Planning Artificial Intelligence",
"Visual Navigation",
"Neural Architecture",
"First Person Views",
"CMP",
"Unified Joint Architecture",
"Spatial Memory",
"Belief Map",
"Differentiable Neural Net Planner",
"Standard Memory",
"Cognitive Mapper And Planer",
"Navigation",
"Planning",
"Robot Kinematics",
"Robot Sensing Systems",
"Visualization"
],
"authors": [
{
"affiliation": null,
"fullName": "Saurabh Gupta",
"givenName": "Saurabh",
"surname": "Gupta",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "James Davidson",
"givenName": "James",
"surname": "Davidson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Sergey Levine",
"givenName": "Sergey",
"surname": "Levine",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Rahul Sukthankar",
"givenName": "Rahul",
"surname": "Sukthankar",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Jitendra Malik",
"givenName": "Jitendra",
"surname": "Malik",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-07-01T00:00:00",
"pubType": "proceedings",
"pages": "7272-7281",
"year": "2017",
"issn": "1063-6919",
"isbn": "978-1-5386-0457-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "0457h263",
"articleId": "12OmNwCsdQf",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "0457h282",
"articleId": "12OmNrIae8x",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/at-equal/2009/3753/0/3753a123",
"title": "Adaptive Anytime Motion Planning for Robust Robot Navigation in Natural Environments",
"doi": null,
"abstractUrl": "/proceedings-article/at-equal/2009/3753a123/12OmNAsk4Ew",
"parentPublication": {
"id": "proceedings/at-equal/2009/3753/0",
"title": "Advanced Technologies for Enhanced Quality of Life",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icarsc/2016/2255/0/07781951",
"title": "Integration of Multiple Events in a Topological Autonomous Navigation System",
"doi": null,
"abstractUrl": "/proceedings-article/icarsc/2016/07781951/12OmNB9t6pY",
"parentPublication": {
"id": "proceedings/icarsc/2016/2255/0",
"title": "2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/irc/2018/4652/0/465201a363",
"title": "Robot Navigation and Path Planning by Means of Rough Mereology",
"doi": null,
"abstractUrl": "/proceedings-article/irc/2018/465201a363/12OmNCmGO1j",
"parentPublication": {
"id": "proceedings/irc/2018/4652/0",
"title": "2018 Second IEEE International Conference on Robotic Computing (IRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aihas/1990/2043/0/00093932",
"title": "Robot task and movement planning",
"doi": null,
"abstractUrl": "/proceedings-article/aihas/1990/00093932/12OmNvjyy3G",
"parentPublication": {
"id": "proceedings/aihas/1990/2043/0",
"title": "1990 Simulation and Planning in High Autonomy Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1992/2720/0/00220095",
"title": "Sensory uncertainty field for mobile robot navigation",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1992/00220095/12OmNy314iS",
"parentPublication": {
"id": "proceedings/robot/1992/2720/0",
"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bracis/2016/3566/0/07839630",
"title": "Transforming Multi-agent Planning Into Single-Agent Planning Using Best-Cost Strategy",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2016/07839630/12OmNyQYttI",
"parentPublication": {
"id": "proceedings/bracis/2016/3566/0",
"title": "2016 5th Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isic/1988/2012/0/00065453",
"title": "Planning compliant motion strategies",
"doi": null,
"abstractUrl": "/proceedings-article/isic/1988/00065453/12OmNyrqzqh",
"parentPublication": {
"id": "proceedings/isic/1988/2012/0",
"title": "Proceedings 1988 IEEE International Symposium on Intelligent Control",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iciibms/2018/7516/3/08549954",
"title": "Research on Laser Navigation Mapping and Path Planning of Tracked Mobile Robot Based on Hector SLAM",
"doi": null,
"abstractUrl": "/proceedings-article/iciibms/2018/08549954/17D45XDIXWP",
"parentPublication": {
"id": "proceedings/iciibms/2018/7516/3",
"title": "2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2022/8739/0/873900e620",
"title": "Coupling Vision and Proprioception for Navigation of Legged Robots",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2022/873900e620/1G56AR1YWqY",
"parentPublication": {
"id": "proceedings/cvprw/2022/8739/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2022/8739/0/873900b894",
"title": "Coupling Vision and Proprioception for Navigation of Legged Robots",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2022/873900b894/1G56YFpGPNS",
"parentPublication": {
"id": "proceedings/cvprw/2022/8739/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzw8jh3",
"title": "Proceedings. 1988 IEEE International Conference on Robotics and Automation",
"acronym": "robot",
"groupId": "1000639",
"volume": "0",
"displayVolume": "0",
"year": "1988",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvDqsF6",
"doi": "10.1109/ROBOT.1988.12305",
"title": "Path planning among moving obstacles using spatial indexing",
"normalizedTitle": "Path planning among moving obstacles using spatial indexing",
"abstract": "A method is presented for planning a path in the presence of moving obstacles. Given a set of polygonal moving obstacles, a path is generated for a mobile robot that navigates in the two-dimensional plane. Time is included as one of the dimensions of the model world. This allows the moving obstacles to be regarded as stationary in the extended world. For a solution to be feasible, the robot must not collide with any other moving obstacles and must navigate within the predetermined range of velocity, acceleration, and centrifugal force. A spatial index is used to facilitate geometric search for the path-planning task. Computer simulation results are presented to illustrate the feasibility of this approach.<>",
"abstracts": [
{
"abstractType": "Regular",
"content": "A method is presented for planning a path in the presence of moving obstacles. Given a set of polygonal moving obstacles, a path is generated for a mobile robot that navigates in the two-dimensional plane. Time is included as one of the dimensions of the model world. This allows the moving obstacles to be regarded as stationary in the extended world. For a solution to be feasible, the robot must not collide with any other moving obstacles and must navigate within the predetermined range of velocity, acceleration, and centrifugal force. A spatial index is used to facilitate geometric search for the path-planning task. Computer simulation results are presented to illustrate the feasibility of this approach.<>",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "A method is presented for planning a path in the presence of moving obstacles. Given a set of polygonal moving obstacles, a path is generated for a mobile robot that navigates in the two-dimensional plane. Time is included as one of the dimensions of the model world. This allows the moving obstacles to be regarded as stationary in the extended world. For a solution to be feasible, the robot must not collide with any other moving obstacles and must navigate within the predetermined range of velocity, acceleration, and centrifugal force. A spatial index is used to facilitate geometric search for the path-planning task. Computer simulation results are presented to illustrate the feasibility of this approach.",
"fno": "00012305",
"keywords": [
"Computational Geometry",
"Navigation",
"Position Control",
"Robots",
"2 D Plane",
"Moving Obstacles",
"Spatial Indexing",
"Planning A Path",
"Mobile Robot",
"Geometric Search",
"Path Planning",
"Indexing",
"Mobile Robots",
"Navigation",
"Acceleration",
"Computer Science",
"Robotics And Automation",
"Educational Institutions",
"Spatial Indexes",
"Computer Simulation"
],
"authors": [
{
"affiliation": "Dept. of Comput. Sci., Maryland Univ., College Park, MD, USA",
"fullName": "K. Fujimura",
"givenName": "K.",
"surname": "Fujimura",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Dept. of Comput. Sci., Maryland Univ., College Park, MD, USA",
"fullName": "H. Samet",
"givenName": "H.",
"surname": "Samet",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "robot",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "1988-01-01T00:00:00",
"pubType": "proceedings",
"pages": "1662,1663,1664,1665,1666,1667",
"year": "1988",
"issn": null,
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "00012304",
"articleId": "12OmNAkWvrB",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "00012306",
"articleId": "12OmNs0C9SS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cim/1990/1966/0/00128167",
"title": "Multi-robot path planning among moving obstacles using multivalue codes",
"doi": null,
"abstractUrl": "/proceedings-article/cim/1990/00128167/12OmNAJVcFK",
"parentPublication": {
"id": "proceedings/cim/1990/1966/0",
"title": "1990 Rensselaer's Second International Conference on Computer Integrated Manufacturing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ems/2009/3886/0/3886a060",
"title": "Using Particle Swarm Optimization for Robot Path Planning in Dynamic Environments with Moving Obstacles and Target",
"doi": null,
"abstractUrl": "/proceedings-article/ems/2009/3886a060/12OmNAMtAOq",
"parentPublication": {
"id": "proceedings/ems/2009/3886/0",
"title": "Computer Modeling and Simulation, UKSIM European Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1988/0862/0/00196325",
"title": "Accessibility: a new approach to path planning among moving obstacles",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1988/00196325/12OmNAkWvJw",
"parentPublication": {
"id": "proceedings/cvpr/1988/0862/0",
"title": "Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1992/2720/0/00220060",
"title": "Using skeletons for nonholonomic path planning among obstacles",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1992/00220060/12OmNwe2Ivw",
"parentPublication": {
"id": "proceedings/robot/1992/2720/0",
"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/tai/1989/1984/0/00065376",
"title": "Differential A*: an adaptive search method illustrated with robot path planning for moving obstacles and goals, and an uncertain environment",
"doi": null,
"abstractUrl": "/proceedings-article/tai/1989/00065376/12OmNxGj9L5",
"parentPublication": {
"id": "proceedings/tai/1989/1984/0",
"title": "IEEE International Workshop on Tools for Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1991/2163/0/00131735",
"title": "On dynamic motion planning problems",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1991/00131735/12OmNyQ7FSx",
"parentPublication": {
"id": "proceedings/robot/1991/2163/0",
"title": "Proceedings. 1991 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/caia/1988/0837/0/00196085",
"title": "Free space modeling and geometric motion planning under unexpected obstacles",
"doi": null,
"abstractUrl": "/proceedings-article/caia/1988/00196085/12OmNzcPAvd",
"parentPublication": {
"id": "proceedings/caia/1988/0837/0",
"title": "Proceedings. The Fourth Conference on Artificial Intelligence Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1994/6265/1/00576300",
"title": "Using multi-layer distance maps for motion planning on surfaces with moving obstacles",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1994/00576300/12OmNzmclAn",
"parentPublication": {
"id": "proceedings/icpr/1994/6265/1",
"title": "Proceedings of 12th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1992/2720/0/00220094",
"title": "Motion planning for a robot and a movable object amidst polygonal obstacles",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1992/00220094/12OmNzvz6N0",
"parentPublication": {
"id": "proceedings/robot/1992/2720/0",
"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1989/1938/0/00100129",
"title": "Time-minimal paths among moving obstacles",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1989/00100129/12OmNzyp60h",
"parentPublication": {
"id": "proceedings/robot/1989/1938/0",
"title": "1989 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNwwMf3z",
"title": "2010 Second WRI Global Congress on Intelligent Systems (GCIS 2010)",
"acronym": "gcis",
"groupId": "1002842",
"volume": "3",
"displayVolume": "3",
"year": "2010",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNvRU0f9",
"doi": "10.1109/GCIS.2010.166",
"title": "Path Planning for UAV in Radar Network Area",
"normalizedTitle": "Path Planning for UAV in Radar Network Area",
"abstract": "This paper mainly introduces a path planning algorithm for the unmanned aerial vehicles (UAVs) to avoid radar network. The radar network contains several radars which have different detection ranges. In this algorithm, firstly, according to the theory of the Delaunay triangulations, a directed graph is constructed based on the locations and the detection ranges of the radars. Secondly, the Dijkstra algorithm is used to search an initial path for the UAV. Thirdly, the optimization approach is used to calculate the optimal control vector which the UAV needs when the UAV navigates to the goal position along the initial path. The simulation results showed that the paths which are planned by the algorithm not only could guide the UAV to avoid the radar network, but also satisfy some task-required constraints, which include temporal, spatial, and UAV maneuverability constraints.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper mainly introduces a path planning algorithm for the unmanned aerial vehicles (UAVs) to avoid radar network. The radar network contains several radars which have different detection ranges. In this algorithm, firstly, according to the theory of the Delaunay triangulations, a directed graph is constructed based on the locations and the detection ranges of the radars. Secondly, the Dijkstra algorithm is used to search an initial path for the UAV. Thirdly, the optimization approach is used to calculate the optimal control vector which the UAV needs when the UAV navigates to the goal position along the initial path. The simulation results showed that the paths which are planned by the algorithm not only could guide the UAV to avoid the radar network, but also satisfy some task-required constraints, which include temporal, spatial, and UAV maneuverability constraints.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper mainly introduces a path planning algorithm for the unmanned aerial vehicles (UAVs) to avoid radar network. The radar network contains several radars which have different detection ranges. In this algorithm, firstly, according to the theory of the Delaunay triangulations, a directed graph is constructed based on the locations and the detection ranges of the radars. Secondly, the Dijkstra algorithm is used to search an initial path for the UAV. Thirdly, the optimization approach is used to calculate the optimal control vector which the UAV needs when the UAV navigates to the goal position along the initial path. The simulation results showed that the paths which are planned by the algorithm not only could guide the UAV to avoid the radar network, but also satisfy some task-required constraints, which include temporal, spatial, and UAV maneuverability constraints.",
"fno": "05709370",
"keywords": [
"Aerospace Control",
"Mesh Generation",
"Optimal Control",
"Optimisation",
"Path Planning",
"Remotely Operated Vehicles",
"Path Planning",
"UAV",
"Radar Network Area",
"Unmanned Aerial Vehicles",
"Delaunay Triangulations",
"Directed Graph",
"Dijkstra Algorithm",
"Optimization Approach",
"Optimal Control Vector",
"Unmanned Aerial Vehicles",
"Radar Detection",
"Navigation",
"Path Planning",
"Heuristic Algorithms",
"Optimal Control",
"UAV",
"Path Planning",
"Delaunay Triangulation",
"Dijkstra Algorithm",
"Optimal Control"
],
"authors": [
{
"affiliation": null,
"fullName": "Fu Xiao-wei",
"givenName": "Fu",
"surname": "Xiao-wei",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Liu Zhong",
"givenName": "Liu",
"surname": "Zhong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Gao Xiao-guang",
"givenName": "Gao",
"surname": "Xiao-guang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "gcis",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2010-12-01T00:00:00",
"pubType": "proceedings",
"pages": "260-263",
"year": "2010",
"issn": "2155-6083",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "05709369",
"articleId": "12OmNxuFBsc",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "05709371",
"articleId": "12OmNzC5T1R",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ihmsc/2010/4151/2/4151b258",
"title": "An Improved Heuristic Algorithm for UAV Path Planning in 3D Environment",
"doi": null,
"abstractUrl": "/proceedings-article/ihmsc/2010/4151b258/12OmNA0vnS5",
"parentPublication": {
"id": "proceedings/ihmsc/2010/4151/2",
"title": "Intelligent Human-Machine Systems and Cybernetics, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/socpar/2009/3879/0/3879a732",
"title": "Contour Based Path Planning for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain",
"doi": null,
"abstractUrl": "/proceedings-article/socpar/2009/3879a732/12OmNxX3uLq",
"parentPublication": {
"id": "proceedings/socpar/2009/3879/0",
"title": "Soft Computing and Pattern Recognition, International Conference of",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2010/4077/3/4077f106",
"title": "UAV Path Planning Based on Bidirectional Sparse A* Search Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2010/4077f106/12OmNyUnEBm",
"parentPublication": {
"id": "proceedings/icicta/2010/4077/3",
"title": "Intelligent Computation Technology and Automation, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscmi/2016/3696/0/3696a053",
"title": "Fireworks Algorithm with New Feasibility-Rules in Solving UAV Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/iscmi/2016/3696a053/12OmNzn391K",
"parentPublication": {
"id": "proceedings/iscmi/2016/3696/0",
"title": "2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ises/2022/9922/0/992200a528",
"title": "DoA Estimation for Micro and Nano UAV Targets using AWR2243 Cascaded Imaging Radar",
"doi": null,
"abstractUrl": "/proceedings-article/ises/2022/992200a528/1KrgrOxE52w",
"parentPublication": {
"id": "proceedings/ises/2022/9922/0",
"title": "2022 IEEE International Symposium on Smart Electronic Systems (iSES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icctec/2017/5784/0/578400b200",
"title": "Research on Autonomous Obstacle Avoidance Flight Path Planning of Rotating Wing UAV",
"doi": null,
"abstractUrl": "/proceedings-article/icctec/2017/578400b200/1cks8UnHQS4",
"parentPublication": {
"id": "proceedings/icctec/2017/5784/0",
"title": "2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/irc/2020/5237/0/523700a490",
"title": "Detection of loaded and unloaded UAV using deep neural network",
"doi": null,
"abstractUrl": "/proceedings-article/irc/2020/523700a490/1pP3SRWeZ20",
"parentPublication": {
"id": "proceedings/irc/2020/5237/0",
"title": "2020 Fourth IEEE International Conference on Robotic Computing (IRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2020/0445/0/044500a006",
"title": "A multi-critic deep deterministic policy gradient UAV path planning",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2020/044500a006/1t90v07Rh0Q",
"parentPublication": {
"id": "proceedings/cis/2020/0445/0",
"title": "2020 16th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icis/2019/0801/0/09481914",
"title": "UAV Flight Path Planning When Considering Coverage Radius of UAV",
"doi": null,
"abstractUrl": "/proceedings-article/icis/2019/09481914/1vg7Ak64GgU",
"parentPublication": {
"id": "proceedings/icis/2019/0801/0",
"title": "2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscsic/2021/1627/0/162700a001",
"title": "A Novel Method for Multi-UAV Cooperative Reconnaissance Mission Planning in Denied Environment",
"doi": null,
"abstractUrl": "/proceedings-article/iscsic/2021/162700a001/1zzpn398Hh6",
"parentPublication": {
"id": "proceedings/iscsic/2021/1627/0",
"title": "2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1GZjzUK3Dm8",
"title": "2021 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)",
"acronym": "icmeae",
"groupId": "1803398",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1GZjAuSN89q",
"doi": "10.1109/ICMEAE55138.2021.00011",
"title": "Path Planning Simulation of a quadrotor in ROS/Gazebo using RGPPM",
"normalizedTitle": "Path Planning Simulation of a quadrotor in ROS/Gazebo using RGPPM",
"abstract": "In this article we are interested in planning collision-free trajectories for the autonomous navigation of a quadrotor. Before planning a trajectory, its start and end points are defined. Subsequently, using Resistive Grid Path Planning Methodology (RGPPM), the trajectory is calculated by modeling the displacement space with a mesh of resistances. This methodology has been expanded to plan 3D trajectories due to the movements in the XYZ axis that the quadrotor can perform in space. The computed trajectories are executed in ROS and Gazebo. The results obtained suggest that the trajectories computed by the RGPPM methodology are suitable for the quadrotor to navigate in a collision-free 3D space.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this article we are interested in planning collision-free trajectories for the autonomous navigation of a quadrotor. Before planning a trajectory, its start and end points are defined. Subsequently, using Resistive Grid Path Planning Methodology (RGPPM), the trajectory is calculated by modeling the displacement space with a mesh of resistances. This methodology has been expanded to plan 3D trajectories due to the movements in the XYZ axis that the quadrotor can perform in space. The computed trajectories are executed in ROS and Gazebo. The results obtained suggest that the trajectories computed by the RGPPM methodology are suitable for the quadrotor to navigate in a collision-free 3D space.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this article we are interested in planning collision-free trajectories for the autonomous navigation of a quadrotor. Before planning a trajectory, its start and end points are defined. Subsequently, using Resistive Grid Path Planning Methodology (RGPPM), the trajectory is calculated by modeling the displacement space with a mesh of resistances. This methodology has been expanded to plan 3D trajectories due to the movements in the XYZ axis that the quadrotor can perform in space. The computed trajectories are executed in ROS and Gazebo. The results obtained suggest that the trajectories computed by the RGPPM methodology are suitable for the quadrotor to navigate in a collision-free 3D space.",
"fno": "954000a020",
"keywords": [
"Collision Avoidance",
"Control Engineering Computing",
"Mobile Robots",
"Path Planning",
"Path Planning Simulation",
"Quadrotor",
"Collision Free Trajectories",
"Autonomous Navigation",
"End Points",
"Resistive Grid Path Planning Methodology",
"Displacement Space",
"Plan 3 D",
"Computed Trajectories",
"RGPPM Methodology",
"Collision Free 3 D",
"Solid Modeling",
"Three Dimensional Displays",
"Mechatronics",
"Navigation",
"Computational Modeling",
"Trajectory",
"Planning",
"Quadrotor",
"Path Planning",
"Movil Robotic",
"ROS",
"Gazebo"
],
"authors": [
{
"affiliation": "Universidad Veracruzana,Artificial Intelligence Research Institute,Xalapa, Veracruz,Mexico",
"fullName": "Sergio Hernandez-Mendez",
"givenName": "Sergio",
"surname": "Hernandez-Mendez",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Instituto Tecnológico Superior de Misantla,Doctorado en Ciencias de la Ingeniería Misantla,Veracruz,México",
"fullName": "Carlos Hernandez-Mejia",
"givenName": "Carlos",
"surname": "Hernandez-Mejia",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Universidad de Xalapa,School of Engineering,Xalapa, Veracruz,Mexico",
"fullName": "Derek Benjamin Herrera Olea",
"givenName": "Derek Benjamin",
"surname": "Herrera Olea",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Universidad Veracruzana,Artificial Intelligence Research Institute,Xalapa, Veracruz,Mexico",
"fullName": "Antonio Marin-Hernandez",
"givenName": "Antonio",
"surname": "Marin-Hernandez",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Universidad Veracruzana,Artificial Intelligence Research Institute,Xalapa, Veracruz,Mexico",
"fullName": "Homero Vladimir Rios-Figueroa",
"givenName": "Homero Vladimir",
"surname": "Rios-Figueroa",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icmeae",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-11-01T00:00:00",
"pubType": "proceedings",
"pages": "20-25",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-9540-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "954000a015",
"articleId": "1GZjFbd20AE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "954000a026",
"articleId": "1GZjG2clm3m",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/sitis/2013/3211/0/3211a726",
"title": "Global Path Planning for Autonomous Mobile Robot Using Genetic Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/sitis/2013/3211a726/12OmNBNM92n",
"parentPublication": {
"id": "proceedings/sitis/2013/3211/0",
"title": "2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/is3c/2016/3071/0/3071a628",
"title": "Analysis of a Quadrotor with a Two-Degree-of-Freedom Robotic Arm",
"doi": null,
"abstractUrl": "/proceedings-article/is3c/2016/3071a628/12OmNBPtJGu",
"parentPublication": {
"id": "proceedings/is3c/2016/3071/0",
"title": "2016 International Symposium on Computer, Consumer and Control (IS3C)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/achi/2008/3086/0/3086a285",
"title": "Obstacle Avoidance Path Planning for Mobile Robot Based on Multi Colony Ant Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/achi/2008/3086a285/12OmNCzb9zY",
"parentPublication": {
"id": "proceedings/achi/2008/3086/0",
"title": "International Conference on Advances in Computer-Human Interaction",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1988/0852/0/00012127",
"title": "Collision detection for planning collision-free motion of two robot arms",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1988/00012127/12OmNCzsKGC",
"parentPublication": {
"id": "proceedings/robot/1988/0852/0",
"title": "Proceedings. 1988 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aihas/1990/2043/0/00093932",
"title": "Robot task and movement planning",
"doi": null,
"abstractUrl": "/proceedings-article/aihas/1990/00093932/12OmNvjyy3G",
"parentPublication": {
"id": "proceedings/aihas/1990/2043/0",
"title": "1990 Simulation and Planning in High Autonomy Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dcabes/2021/2889/0/288900a073",
"title": "Research on Path Planning Algorithm of Intelligent Wheeled Robot Based on ROS",
"doi": null,
"abstractUrl": "/proceedings-article/dcabes/2021/288900a073/1AqwA10KNjO",
"parentPublication": {
"id": "proceedings/dcabes/2021/2889/0",
"title": "2021 20th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2022/9744/0/974400a818",
"title": "Imitation Learning-Based Drone Motion Planning in Dense Obstacle Scenarios",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2022/974400a818/1MrFUfPjOJq",
"parentPublication": {
"id": "proceedings/ictai/2022/9744/0",
"title": "2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccps/2020/5501/0/09095999",
"title": "Socially-Aware Robot Planning via Bandit Human Feedback",
"doi": null,
"abstractUrl": "/proceedings-article/iccps/2020/09095999/1jXvvaZWovu",
"parentPublication": {
"id": "proceedings/iccps/2020/5501/0",
"title": "2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbaie/2020/6499/0/09196451",
"title": "Optimal Path Planning for Multi-UAV Based on Pseudo-spectral Method",
"doi": null,
"abstractUrl": "/proceedings-article/icbaie/2020/09196451/1n90UArMAbm",
"parentPublication": {
"id": "proceedings/icbaie/2020/6499/0",
"title": "2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/irc/2020/5237/0/523700a226",
"title": "SAFESTOP: Disturbance Handling in Prioritized Multi-robot Trajectory Planning",
"doi": null,
"abstractUrl": "/proceedings-article/irc/2020/523700a226/1pP3S6MyBhu",
"parentPublication": {
"id": "proceedings/irc/2020/5237/0",
"title": "2020 Fourth IEEE International Conference on Robotic Computing (IRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1tmhi3ly74c",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"acronym": "icpr",
"groupId": "1000545",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1tmhosoXTLq",
"doi": "10.1109/ICPR48806.2021.9412869",
"title": "EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial Pattern",
"normalizedTitle": "EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial Pattern",
"abstract": "Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial Pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial Pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. This proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial Pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial Pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. This proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial Pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial Pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. This proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.",
"fno": "09412869",
"keywords": [
"Brain Computer Interfaces",
"Cognition",
"Electroencephalography",
"Feature Extraction",
"Medical Signal Processing",
"Neurophysiology",
"Signal Classification",
"Cognitive State Assessment",
"Deep Ensemble Model",
"Filter Bank Common Spatial Pattern",
"Poor Spatial Resolution",
"Spatial Features",
"Common Spatial Pattern Algorithm",
"Solid Modeling",
"Computational Modeling",
"Filter Banks",
"Predictive Models",
"Brain Modeling",
"Feature Extraction",
"Electroencephalography"
],
"authors": [
{
"affiliation": "Indian Institute of Technology,Roorkee,India",
"fullName": "Debashis Das Chakladar",
"givenName": "Debashis Das",
"surname": "Chakladar",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute of Engineering and Management,Kolkata,India",
"fullName": "Shubhashis Dey",
"givenName": "Shubhashis",
"surname": "Dey",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Indian Institute of Technology,Roorkee,India",
"fullName": "Partha Pratim Roy",
"givenName": "Partha Pratim",
"surname": "Roy",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Osaka Prefecture University,Japan",
"fullName": "Masakazu Iwamura",
"givenName": "Masakazu",
"surname": "Iwamura",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-01-01T00:00:00",
"pubType": "proceedings",
"pages": "4107-4114",
"year": "2021",
"issn": "1051-4651",
"isbn": "978-1-7281-8808-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09412303",
"articleId": "1tmjonDh8m4",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09412749",
"articleId": "1tmiyCC3hba",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cw/2015/9403/0/9403a161",
"title": "Prediction of Human Cognitive Abilities Based on EEG Measurements",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2015/9403a161/12OmNAXxX0B",
"parentPublication": {
"id": "proceedings/cw/2015/9403/0",
"title": "2015 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isecs/2010/8231/0/05557468",
"title": "Research of Drowsiness in Driving Based on EEG",
"doi": null,
"abstractUrl": "/proceedings-article/isecs/2010/05557468/12OmNxV4iuW",
"parentPublication": {
"id": "proceedings/isecs/2010/8231/0",
"title": "2010 Third International Symposium on Electronic Commerce and Security (ISECS 2010)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2015/03/06832647",
"title": "Probabilistic Common Spatial Patterns for Multichannel EEG Analysis",
"doi": null,
"abstractUrl": "/journal/tp/2015/03/06832647/13rRUxYIMWq",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaice/2021/2186/0/218600a660",
"title": "Motor Imagery Analysis Based on Filter Bank Common Spatial Pattern",
"doi": null,
"abstractUrl": "/proceedings-article/icaice/2021/218600a660/1Et4supbfGw",
"parentPublication": {
"id": "proceedings/icaice/2021/2186/0",
"title": "2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icceai/2022/6803/0/680300a684",
"title": "Improved Graph Convolutional Neural Networks based on Granger Causality Analysis for EEG Emotion Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/icceai/2022/680300a684/1FUVDYsfCus",
"parentPublication": {
"id": "proceedings/icceai/2022/6803/0",
"title": "2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10020818",
"title": "Fast Fourier Transform and Ensemble Model to Classify Epileptic EEG Signals",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10020818/1KfRNRGA7du",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cyberc/2019/2542/0/254200a464",
"title": "Lightweight EEG Classification Model Based on EEG-sensor with Few Channels",
"doi": null,
"abstractUrl": "/proceedings-article/cyberc/2019/254200a464/1gjRZe92z84",
"parentPublication": {
"id": "proceedings/cyberc/2019/2542/0",
"title": "2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmew/2020/1485/0/09106021",
"title": "Multi-CNN Feature Fusion for Efficient EEG Classification",
"doi": null,
"abstractUrl": "/proceedings-article/icmew/2020/09106021/1kwqIGnLlcc",
"parentPublication": {
"id": "proceedings/icmew/2020/1485/0",
"title": "2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dependsys/2020/7651/0/765100a073",
"title": "Cognitive Privacy: AI-enabled Privacy using EEG Signals in the Internet of Things",
"doi": null,
"abstractUrl": "/proceedings-article/dependsys/2020/765100a073/1rsiWKtK2Va",
"parentPublication": {
"id": "proceedings/dependsys/2020/7651/0",
"title": "2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2021/4261/0/09635427",
"title": "Sparse Graph-based Representations of SSVEP Responses Under the Variational Bayesian Framework",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2021/09635427/1zmvmRsSc9O",
"parentPublication": {
"id": "proceedings/bibe/2021/4261/0",
"title": "2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1x4YWKn7RSw",
"title": "2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)",
"acronym": "icvris",
"groupId": "1828444",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1x4Z4G0n9Cw",
"doi": "10.1109/ICVRIS51417.2020.00230",
"title": "Path Planning Based on Improved Ant Colony Algorithm",
"normalizedTitle": "Path Planning Based on Improved Ant Colony Algorithm",
"abstract": "In order to overcome the problems of low accuracy and long time-consuming in traditional heuristic path planning algorithm, a new path planning method is proposed based on the improved Ant Colony (AC) algorithm. Different from the traditional AC algorithm worked with fix pheromone, the updatable pheromone is adopted by the improved AC algorithm to increase the diversity performance. Based on this advantage, the proposed path planning method has better accuracy and convergence performance. Simulation results show that the time-consuming of the proposed method is reduced effectively. And, the shortest path length of the proposed method is also shorter than the traditional heuristic path planning algorithm obviously.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In order to overcome the problems of low accuracy and long time-consuming in traditional heuristic path planning algorithm, a new path planning method is proposed based on the improved Ant Colony (AC) algorithm. Different from the traditional AC algorithm worked with fix pheromone, the updatable pheromone is adopted by the improved AC algorithm to increase the diversity performance. Based on this advantage, the proposed path planning method has better accuracy and convergence performance. Simulation results show that the time-consuming of the proposed method is reduced effectively. And, the shortest path length of the proposed method is also shorter than the traditional heuristic path planning algorithm obviously.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In order to overcome the problems of low accuracy and long time-consuming in traditional heuristic path planning algorithm, a new path planning method is proposed based on the improved Ant Colony (AC) algorithm. Different from the traditional AC algorithm worked with fix pheromone, the updatable pheromone is adopted by the improved AC algorithm to increase the diversity performance. Based on this advantage, the proposed path planning method has better accuracy and convergence performance. Simulation results show that the time-consuming of the proposed method is reduced effectively. And, the shortest path length of the proposed method is also shorter than the traditional heuristic path planning algorithm obviously.",
"fno": "963600a946",
"keywords": [
"Ant Colony Optimisation",
"Optimisation",
"Path Planning",
"Search Problems",
"Path Planning Method",
"Convergence Performance",
"Shortest Path Length",
"Improved Ant Colony Algorithm",
"Long Time Consuming",
"Traditional Heuristic Path Planning Algorithm",
"Traditional AC Algorithm",
"Fix Pheromone",
"Updatable Pheromone",
"Improved AC Algorithm",
"Solid Modeling",
"Heuristic Algorithms",
"Simulation",
"Software Algorithms",
"Virtual Reality",
"Path Planning",
"Software",
"Ant Colony",
"The Diversity",
"Path Planning"
],
"authors": [
{
"affiliation": "Xijing University,School of Information Engineering,Xi’an,China,710123",
"fullName": "Yushuai Zhang",
"givenName": "Yushuai",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xijing University,School of Information Engineering,Xi’an,China,710123",
"fullName": "Jianxin Guo",
"givenName": "Jianxin",
"surname": "Guo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xijing University,School of Information Engineering,Xi’an,China,710123",
"fullName": "Rui Zhu",
"givenName": "Rui",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xijing University,School of Information Engineering,Xi’an,China,710123",
"fullName": "Zhengyang Zhao",
"givenName": "Zhengyang",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Xijing University,School of Information Engineering,Xi’an,China,710123",
"fullName": "Liping Wang",
"givenName": "Liping",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icvris",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-07-01T00:00:00",
"pubType": "proceedings",
"pages": "946-949",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-9636-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "963600a941",
"articleId": "1x4ZdFaGHe0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "963600a950",
"articleId": "1x4ZcwJ2QNi",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iciicii/2017/2434/0/2434a001",
"title": "Improved Ant Colony Optimization Algorithm for Intelligent Vehicle Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/iciicii/2017/2434a001/12OmNrJAe9o",
"parentPublication": {
"id": "proceedings/iciicii/2017/2434/0",
"title": "2017 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cyberc/2018/0974/0/097400a106",
"title": "Improved Ant Colony Optimization for Ground Robot 3D Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/cyberc/2018/097400a106/17QjJdubBfC",
"parentPublication": {
"id": "proceedings/cyberc/2018/0974/0",
"title": "2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wcmeim/2021/2172/0/217200a366",
"title": "Optimal path planning of mobile robot based on improved ant colony algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/wcmeim/2021/217200a366/1ANLr2PqmCk",
"parentPublication": {
"id": "proceedings/wcmeim/2021/2172/0",
"title": "2021 4th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/insai/2021/0859/0/085900a095",
"title": "An Improved Ant Colony Optimization Algorithm for Multi-Agent Path Planning",
"doi": null,
"abstractUrl": "/proceedings-article/insai/2021/085900a095/1CHwYOLlNde",
"parentPublication": {
"id": "proceedings/insai/2021/0859/0",
"title": "2021 International Conference on Networking Systems of AI (INSAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2022/4627/0/462700a043",
"title": "Path planning algorithm based on hybrid A* and adaptive ant colony optimization",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2022/462700a043/1M9pYII7opa",
"parentPublication": {
"id": "proceedings/cis/2022/4627/0",
"title": "2022 18th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbaie/2020/6499/0/09196420",
"title": "A Path Planning Method of Logistics Robot Based on Improved Ant Colony Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icbaie/2020/09196420/1n90YGDzyXm",
"parentPublication": {
"id": "proceedings/icbaie/2020/6499/0",
"title": "2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acctcs/2021/1538/0/153800a001",
"title": "Path planning of intelligent factory based on improved ant colony algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/acctcs/2021/153800a001/1t90kDcYR8Y",
"parentPublication": {
"id": "proceedings/acctcs/2021/1538/0",
"title": "2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icedme/2021/3596/0/359600a176",
"title": "Global path planning of unmanned boat based on improved ant colony algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icedme/2021/359600a176/1tMPPwlR5kI",
"parentPublication": {
"id": "proceedings/icedme/2021/3596/0",
"title": "2021 4th International Conference on Electron Device and Mechanical Engineering (ICEDME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icoias/2021/4195/0/419500a357",
"title": "UAV Path Planning Based on an Improved Ant Colony Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icoias/2021/419500a357/1wG6cTRBUIM",
"parentPublication": {
"id": "proceedings/icoias/2021/4195/0",
"title": "2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaa/2021/3730/0/373000a171",
"title": "Robot Path Planning Based on Fusion Improved Ant Colony Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icaa/2021/373000a171/1zL201oApP2",
"parentPublication": {
"id": "proceedings/icaa/2021/3730/0",
"title": "2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNvjgWMW",
"title": "2013 Brazilian Conference on Intelligent Systems (BRACIS)",
"acronym": "bracis",
"groupId": "1803430",
"volume": "0",
"displayVolume": "0",
"year": "2013",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNA1DMkO",
"doi": "10.1109/BRACIS.2013.36",
"title": "Terminology Learning through Taxonomy Discovery",
"normalizedTitle": "Terminology Learning through Taxonomy Discovery",
"abstract": "Description Logics based languages have emerged as the standard knowledge representation scheme for ontologies. Typically, an ontology formalizes a number of dependent and related concepts in a domain, encompassed as a terminology. As defining such terminologies manually is a complex, time consuming and error-prone task, there is great interest and even demands for methods that learn terminologies automatically. Learning a terminology in Descriptions Logics concerns to learn several related concepts. This process would greatly benefit of an ideal order to determine which concept should be learned before another concept. Arguably, such an order would yield rich and readable terminologies, as previously, and interrelated concepts formerly learned could be used to induce the description of further concepts. In this work, we contribute with a formal definition of the concept and terminology learning problems and from such definitions we devise an algorithm for finding an ordering through concept taxonomy discovery, that should be followed when learning several related concepts. We show through an experiment that by following the order detected by the algorithm, we are able to afford a more readable terminology than methods that do not conceive an ideal order or do not learn concepts in a dependent way.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Description Logics based languages have emerged as the standard knowledge representation scheme for ontologies. Typically, an ontology formalizes a number of dependent and related concepts in a domain, encompassed as a terminology. As defining such terminologies manually is a complex, time consuming and error-prone task, there is great interest and even demands for methods that learn terminologies automatically. Learning a terminology in Descriptions Logics concerns to learn several related concepts. This process would greatly benefit of an ideal order to determine which concept should be learned before another concept. Arguably, such an order would yield rich and readable terminologies, as previously, and interrelated concepts formerly learned could be used to induce the description of further concepts. In this work, we contribute with a formal definition of the concept and terminology learning problems and from such definitions we devise an algorithm for finding an ordering through concept taxonomy discovery, that should be followed when learning several related concepts. We show through an experiment that by following the order detected by the algorithm, we are able to afford a more readable terminology than methods that do not conceive an ideal order or do not learn concepts in a dependent way.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Description Logics based languages have emerged as the standard knowledge representation scheme for ontologies. Typically, an ontology formalizes a number of dependent and related concepts in a domain, encompassed as a terminology. As defining such terminologies manually is a complex, time consuming and error-prone task, there is great interest and even demands for methods that learn terminologies automatically. Learning a terminology in Descriptions Logics concerns to learn several related concepts. This process would greatly benefit of an ideal order to determine which concept should be learned before another concept. Arguably, such an order would yield rich and readable terminologies, as previously, and interrelated concepts formerly learned could be used to induce the description of further concepts. In this work, we contribute with a formal definition of the concept and terminology learning problems and from such definitions we devise an algorithm for finding an ordering through concept taxonomy discovery, that should be followed when learning several related concepts. We show through an experiment that by following the order detected by the algorithm, we are able to afford a more readable terminology than methods that do not conceive an ideal order or do not learn concepts in a dependent way.",
"fno": "5092a169",
"keywords": [
"Terminology",
"Taxonomy",
"Knowledge Based Systems",
"Ontologies",
"Cognition",
"Semantics"
],
"authors": [
{
"affiliation": null,
"fullName": "Raphael Melo",
"givenName": "Raphael",
"surname": "Melo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Kate Revoredo",
"givenName": "Kate",
"surname": "Revoredo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Aline Paes",
"givenName": "Aline",
"surname": "Paes",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "bracis",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2013-10-01T00:00:00",
"pubType": "proceedings",
"pages": "169-174",
"year": "2013",
"issn": null,
"isbn": "978-0-7695-5092-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "5092a163",
"articleId": "12OmNqyUUu8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "5092a175",
"articleId": "12OmNyjtNJp",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/skg/2015/9808/0/9808a217",
"title": "A Hybrid Strategy for Chinese Domain-Specific Terminology Extraction",
"doi": null,
"abstractUrl": "/proceedings-article/skg/2015/9808a217/12OmNqGA59r",
"parentPublication": {
"id": "proceedings/skg/2015/9808/0",
"title": "2015 11th International Conference on Semantics, Knowledge and Grids (SKG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wi-iat/2009/3801/3/3801c271",
"title": "Extracting Chinese-English Bilingual Core Terminology from Parallel Classified Corpora in Special Domain",
"doi": null,
"abstractUrl": "/proceedings-article/wi-iat/2009/3801c271/12OmNqHItO5",
"parentPublication": {
"id": "proceedings/wi-iat/2009/3801/3",
"title": "Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2016/1611/0/07822722",
"title": "Similarity-based algorithms for Disease Terminology Mapping",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2016/07822722/12OmNwekjHY",
"parentPublication": {
"id": "proceedings/bibm/2016/1611/0",
"title": "2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bracis/2014/5618/0/5618a180",
"title": "Syntactic Compression of Description Logics Terminologies",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2014/5618a180/12OmNzUPpjT",
"parentPublication": {
"id": "proceedings/bracis/2014/5618/0",
"title": "2014 Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/seaa/2018/7383/0/738300a478",
"title": "Towards a Terminology Unification in Software Interoperability",
"doi": null,
"abstractUrl": "/proceedings-article/seaa/2018/738300a478/17D45WK5AqF",
"parentPublication": {
"id": "proceedings/seaa/2018/7383/0",
"title": "2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/re/2020/7438/0/09218192",
"title": "Cutting through the Jungle: Disambiguating Model-based Traceability Terminology",
"doi": null,
"abstractUrl": "/proceedings-article/re/2020/09218192/1nMQyUAvTGM",
"parentPublication": {
"id": "proceedings/re/2020/7438/0",
"title": "2020 IEEE 28th International Requirements Engineering Conference (RE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313435",
"title": "Generating Training Data for Concept-Mining for an ‘Interface Terminology’ Annotating Cardiology EHRs",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313435/1qmg2ACuIog",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313186",
"title": "A Lexical-based Formal Concept Analysis Method to Identify Missing Concepts in the NCI Thesaurus",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313186/1qmg3dWdPsk",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313523",
"title": "Can MED-RT Summarization Support Missing Adverse Drug Reactions Discovery?",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313523/1qmg9VyWDJe",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377981",
"title": "Mining Concepts for a COVID Interface Terminology for Annotation of EHRs",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377981/1s64nNN98XK",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNyRxFmk",
"title": "EUROMICRO Conference",
"acronym": "euromicro",
"groupId": "1002914",
"volume": "0",
"displayVolume": "0",
"year": "2005",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNqIzh1a",
"doi": "10.1109/EUROMICRO.2005.8",
"title": "A taxonomy of software component models",
"normalizedTitle": "A taxonomy of software component models",
"abstract": "CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.",
"abstracts": [
{
"abstractType": "Regular",
"content": "CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.",
"fno": "01517731",
"keywords": [
"Object Oriented Programming",
"Software Engineering",
"Software Component Model Taxonomy",
"CBSE Terminology",
"Component Based Software Engineering",
"Taxonomy",
"Terminology",
"Assembly",
"Computer Science",
"Councils",
"Java",
"Fractals",
"Software Engineering",
"Application Software",
"Computer Languages"
],
"authors": [
{
"affiliation": "Sch. of Comput. Sci., Manchester Univ., UK",
"fullName": "Kung-Kiu Lau",
"givenName": null,
"surname": "Kung-Kiu Lau",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Sch. of Comput. Sci., Manchester Univ., UK",
"fullName": "Zheng Wang",
"givenName": null,
"surname": "Zheng Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "euromicro",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2005-01-01T00:00:00",
"pubType": "proceedings",
"pages": "88,89,90,91,92,93,94,95",
"year": "2005",
"issn": null,
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "01517730",
"articleId": "12OmNy2rRXV",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "01517732",
"articleId": "12OmNAhxjDY",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bracis/2013/5092/0/5092a169",
"title": "Terminology Learning through Taxonomy Discovery",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2013/5092a169/12OmNA1DMkO",
"parentPublication": {
"id": "proceedings/bracis/2013/5092/0",
"title": "2013 Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ucc/2013/5152/0/06809342",
"title": "Beyond IaaS and PaaS: An Extended Cloud Taxonomy for Computation, Storage and Networking",
"doi": null,
"abstractUrl": "/proceedings-article/ucc/2013/06809342/12OmNBZHikh",
"parentPublication": {
"id": "proceedings/ucc/2013/5152/0",
"title": "2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2000/206/0/00870409",
"title": "Towards a taxonomy of software connectors",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2000/00870409/12OmNCbU30L",
"parentPublication": {
"id": "proceedings/icse/2000/206/0",
"title": "Proceedings of International Conference on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eurmic/2005/2431/0/01517731",
"title": "A taxonomy of software component models",
"doi": null,
"abstractUrl": "/proceedings-article/eurmic/2005/01517731/12OmNqOwQCq",
"parentPublication": {
"id": "proceedings/eurmic/2005/2431/0",
"title": "Proceedings. 31st Euromicro Conference on Software Engineering and Advanced Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icgse/2016/2680/0/2680a154",
"title": "A Specialized Global Software Engineering Taxonomy for Effort Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/icgse/2016/2680a154/12OmNvUsonE",
"parentPublication": {
"id": "proceedings/icgse/2016/2680/0",
"title": "2016 IEEE 11th International Conference on Global Software Engineering (ICGSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/euromicro/2005/2431/0/24310088",
"title": "A Taxonomy of Software Component Models",
"doi": null,
"abstractUrl": "/proceedings-article/euromicro/2005/24310088/12OmNvxsST0",
"parentPublication": {
"id": "proceedings/euromicro/2005/2431/0",
"title": "EUROMICRO Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2006/2832/0/283201081",
"title": "Software component models",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2006/283201081/12OmNzcPA2w",
"parentPublication": {
"id": "proceedings/icse/2006/2832/0",
"title": "Software Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2007/10/e0709",
"title": "Software Component Models",
"doi": null,
"abstractUrl": "/journal/ts/2007/10/e0709/13rRUxAAT9l",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccbss/2007/2785/0/04127314",
"title": "A Lightweight Taxonomy to Characterize Component-Based Systems",
"doi": null,
"abstractUrl": "/proceedings-article/iccbss/2007/04127314/17D45WHONqT",
"parentPublication": {
"id": "proceedings/iccbss/2007/2785/0",
"title": "2007 International Conference on COTS-Based Software Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2019/3485/0/348500a084",
"title": "A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2019/348500a084/1cYi2xeH3cA",
"parentPublication": {
"id": "proceedings/icalt/2019/3485/2161-377X",
"title": "2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzwpUaK",
"title": "Proceedings. 31st Euromicro Conference on Software Engineering and Advanced Applications",
"acronym": "eurmic",
"groupId": "1002914",
"volume": "0",
"displayVolume": "0",
"year": "2005",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNqOwQCq",
"doi": "10.1109/EUROMICRO.2005.8",
"title": "A taxonomy of software component models",
"normalizedTitle": "A taxonomy of software component models",
"abstract": "CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.",
"abstracts": [
{
"abstractType": "Regular",
"content": "CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.",
"fno": "01517731",
"keywords": [
"Object Oriented Programming",
"Software Engineering",
"Software Component Model Taxonomy",
"CBSE Terminology",
"Component Based Software Engineering",
"Taxonomy",
"Terminology",
"Assembly",
"Computer Science",
"Councils",
"Java",
"Fractals",
"Software Engineering",
"Application Software",
"Computer Languages"
],
"authors": [
{
"affiliation": "Sch. of Comput. Sci., Manchester Univ., UK",
"fullName": "Kung-Kiu Lau",
"givenName": null,
"surname": "Kung-Kiu Lau",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Sch. of Comput. Sci., Manchester Univ., UK",
"fullName": "Zheng Wang",
"givenName": null,
"surname": "Zheng Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "eurmic",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2005-01-01T00:00:00",
"pubType": "proceedings",
"pages": "88,89,90,91,92,93,94,95",
"year": "2005",
"issn": null,
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "01517730",
"articleId": "12OmNBJNKZh",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "01517732",
"articleId": "12OmNyz5JUs",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bracis/2013/5092/0/5092a169",
"title": "Terminology Learning through Taxonomy Discovery",
"doi": null,
"abstractUrl": "/proceedings-article/bracis/2013/5092a169/12OmNA1DMkO",
"parentPublication": {
"id": "proceedings/bracis/2013/5092/0",
"title": "2013 Brazilian Conference on Intelligent Systems (BRACIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ucc/2013/5152/0/06809342",
"title": "Beyond IaaS and PaaS: An Extended Cloud Taxonomy for Computation, Storage and Networking",
"doi": null,
"abstractUrl": "/proceedings-article/ucc/2013/06809342/12OmNBZHikh",
"parentPublication": {
"id": "proceedings/ucc/2013/5152/0",
"title": "2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/euromicro/2005/2431/0/01517731",
"title": "A taxonomy of software component models",
"doi": null,
"abstractUrl": "/proceedings-article/euromicro/2005/01517731/12OmNqIzh1a",
"parentPublication": {
"id": "proceedings/euromicro/2005/2431/0",
"title": "EUROMICRO Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icgse/2016/2680/0/2680a154",
"title": "A Specialized Global Software Engineering Taxonomy for Effort Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/icgse/2016/2680a154/12OmNvUsonE",
"parentPublication": {
"id": "proceedings/icgse/2016/2680/0",
"title": "2016 IEEE 11th International Conference on Global Software Engineering (ICGSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/euromicro/2005/2431/0/24310088",
"title": "A Taxonomy of Software Component Models",
"doi": null,
"abstractUrl": "/proceedings-article/euromicro/2005/24310088/12OmNvxsST0",
"parentPublication": {
"id": "proceedings/euromicro/2005/2431/0",
"title": "EUROMICRO Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2006/2832/0/283201081",
"title": "Software component models",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2006/283201081/12OmNzcPA2w",
"parentPublication": {
"id": "proceedings/icse/2006/2832/0",
"title": "Software Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2007/10/e0709",
"title": "Software Component Models",
"doi": null,
"abstractUrl": "/journal/ts/2007/10/e0709/13rRUxAAT9l",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccbss/2007/2785/0/04127314",
"title": "A Lightweight Taxonomy to Characterize Component-Based Systems",
"doi": null,
"abstractUrl": "/proceedings-article/iccbss/2007/04127314/17D45WHONqT",
"parentPublication": {
"id": "proceedings/iccbss/2007/2785/0",
"title": "2007 International Conference on COTS-Based Software Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09745848",
"title": "A Model for Types and Levels of Automation in Visual Analytics: a Survey, a Taxonomy, and Examples",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09745848/1CbVnSejsjK",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2019/3485/0/348500a084",
"title": "A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2019/348500a084/1cYi2xeH3cA",
"parentPublication": {
"id": "proceedings/icalt/2019/3485/2161-377X",
"title": "2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzBOhKb",
"title": "2008 Third International Conference on Availability, Reliability and Security",
"acronym": "ares",
"groupId": "1001707",
"volume": "0",
"displayVolume": "0",
"year": "2008",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNwGIcBg",
"doi": "10.1109/ARES.2008.38",
"title": "A Revised Taxonomy of Data Collection Mechanisms with a Focus on Intrusion Detection",
"normalizedTitle": "A Revised Taxonomy of Data Collection Mechanisms with a Focus on Intrusion Detection",
"abstract": "Surprisingly few data collection mechanisms have been used for intrusion detection, and most systems rely on network and system call data as input to the detection engine. Even though the quality of log data is vital to the detection process and heavily dependent on the collection mechanism, no extensive survey or taxonomy has beenconducted within the detection field. In this paper, we propose a revised taxonomy which provides a unified terminology and a framework in which data collection mechanisms can be systematically inspected, evaluated, and compared. Since the taxonomy is derived from existing mechanisms, it also provides a useful overview of different types of mechanisms. The paper also suggests areas within data collection where additional work is required.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Surprisingly few data collection mechanisms have been used for intrusion detection, and most systems rely on network and system call data as input to the detection engine. Even though the quality of log data is vital to the detection process and heavily dependent on the collection mechanism, no extensive survey or taxonomy has beenconducted within the detection field. In this paper, we propose a revised taxonomy which provides a unified terminology and a framework in which data collection mechanisms can be systematically inspected, evaluated, and compared. Since the taxonomy is derived from existing mechanisms, it also provides a useful overview of different types of mechanisms. The paper also suggests areas within data collection where additional work is required.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Surprisingly few data collection mechanisms have been used for intrusion detection, and most systems rely on network and system call data as input to the detection engine. Even though the quality of log data is vital to the detection process and heavily dependent on the collection mechanism, no extensive survey or taxonomy has beenconducted within the detection field. In this paper, we propose a revised taxonomy which provides a unified terminology and a framework in which data collection mechanisms can be systematically inspected, evaluated, and compared. Since the taxonomy is derived from existing mechanisms, it also provides a useful overview of different types of mechanisms. The paper also suggests areas within data collection where additional work is required.",
"fno": "3102a624",
"keywords": [
"Taxonomy",
"Intrusion Detection",
"Data Collection"
],
"authors": [
{
"affiliation": null,
"fullName": "Ulf Larson",
"givenName": "Ulf",
"surname": "Larson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Erland Jonsson",
"givenName": "Erland",
"surname": "Jonsson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Stefan Lindskog",
"givenName": "Stefan",
"surname": "Lindskog",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ares",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2008-03-01T00:00:00",
"pubType": "proceedings",
"pages": "624-629",
"year": "2008",
"issn": null,
"isbn": "978-0-7695-3102-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "3102a618",
"articleId": "12OmNAfy7I3",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "3102a630",
"articleId": "12OmNvnOwsI",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/3pgcic/2010/4237/0/4237a535",
"title": "Simple Tree Based Routing for Data Collection Networks",
"doi": null,
"abstractUrl": "/proceedings-article/3pgcic/2010/4237a535/12OmNBTs7sK",
"parentPublication": {
"id": "proceedings/3pgcic/2010/4237/0",
"title": "P2P, Parallel, Grid, Cloud, and Internet Computing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpads/2017/2129/0/212901a773",
"title": "Exploring the Efficiency of Data Collection Schemes in Wireless Sensor Networks",
"doi": null,
"abstractUrl": "/proceedings-article/icpads/2017/212901a773/12OmNwnYG0a",
"parentPublication": {
"id": "proceedings/icpads/2017/2129/0",
"title": "2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acsac/2004/2252/0/22520158",
"title": "Extracting Attack Manifestations to Determine Log Data Requirements for Intrusion Detection",
"doi": null,
"abstractUrl": "/proceedings-article/acsac/2004/22520158/12OmNxGj9JO",
"parentPublication": {
"id": "proceedings/acsac/2004/2252/0",
"title": "Computer Security Applications Conference, Annual",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispan/2008/3125/0/3125a019",
"title": "A Taxonomy of Data Prefetching Mechanisms",
"doi": null,
"abstractUrl": "/proceedings-article/ispan/2008/3125a019/12OmNxaw5an",
"parentPublication": {
"id": "proceedings/ispan/2008/3125/0",
"title": "Parallel Architectures, Algorithms, and Networks, International Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2009/3804/3/3804c440",
"title": "A New Measure for Traffic Data Collection and Processing",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2009/3804c440/12OmNxecRY7",
"parentPublication": {
"id": null,
"title": null,
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aqtr/2018/2205/0/08402710",
"title": "A taxonomy and platform for anomaly detection",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2018/08402710/12OmNzZmZsx",
"parentPublication": {
"id": "proceedings/aqtr/2018/2205/0",
"title": "2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2023/02/09739868",
"title": "A Taxonomy of Inter-Team Coordination Mechanisms in Large-Scale Agile",
"doi": null,
"abstractUrl": "/journal/ts/2023/02/09739868/1BWZjO9PXWg",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csci/2021/5841/0/584100a817",
"title": "Taxonomy for Malware Detection to Enhance the Security of Smart Devices using AI",
"doi": null,
"abstractUrl": "/proceedings-article/csci/2021/584100a817/1EpLjGUW2lO",
"parentPublication": {
"id": "proceedings/csci/2021/5841/0",
"title": "2021 International Conference on Computational Science and Computational Intelligence (CSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2022/5417/0/541700a597",
"title": "A Taxonomy of Security and Defense Mechanisms in Digital Twins-based Cyber-Physical Systems",
"doi": null,
"abstractUrl": "/proceedings-article/ithings-greencom-cpscom-smartdata-cybermatics/2022/541700a597/1HcmWeWMT16",
"parentPublication": {
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2022/5417/0",
"title": "2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2021/2463/0/246300b085",
"title": "Updating the Taxonomy of Intrusion Detection Systems",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2021/246300b085/1wLciavIAYo",
"parentPublication": {
"id": "proceedings/compsac/2021/2463/0",
"title": "2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1cYi06q10li",
"title": "2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)",
"acronym": "icalt",
"groupId": "1000009",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1cYi2xeH3cA",
"doi": "10.1109/ICALT.2019.00028",
"title": "A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation",
"normalizedTitle": "A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation",
"abstract": "Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This paper aims to create a taxonomy for the game elements, based on gamification experts' opinions. After a brief review from existing work, we extract first the game elements from the current state of the art, and then evaluate them via a survey with 19 gamification and education experts. The resulting taxonomy taxonomy included the description of 21 game elements and their quantitative and qualitative evaluation by the experts. Overall, the proposed taxonomy was in general well accepted by most of the experts. They also suggested expanding it with the inclusion of Narrative and Storytelling game elements. Thus, the main contribution of this paper is proposing a new, confirmed taxonomy to standardise the terminology used to define the game elements as a mean to design and deploy gamification strategies in the educational domain.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This paper aims to create a taxonomy for the game elements, based on gamification experts' opinions. After a brief review from existing work, we extract first the game elements from the current state of the art, and then evaluate them via a survey with 19 gamification and education experts. The resulting taxonomy taxonomy included the description of 21 game elements and their quantitative and qualitative evaluation by the experts. Overall, the proposed taxonomy was in general well accepted by most of the experts. They also suggested expanding it with the inclusion of Narrative and Storytelling game elements. Thus, the main contribution of this paper is proposing a new, confirmed taxonomy to standardise the terminology used to define the game elements as a mean to design and deploy gamification strategies in the educational domain.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This paper aims to create a taxonomy for the game elements, based on gamification experts' opinions. After a brief review from existing work, we extract first the game elements from the current state of the art, and then evaluate them via a survey with 19 gamification and education experts. The resulting taxonomy taxonomy included the description of 21 game elements and their quantitative and qualitative evaluation by the experts. Overall, the proposed taxonomy was in general well accepted by most of the experts. They also suggested expanding it with the inclusion of Narrative and Storytelling game elements. Thus, the main contribution of this paper is proposing a new, confirmed taxonomy to standardise the terminology used to define the game elements as a mean to design and deploy gamification strategies in the educational domain.",
"fno": "348500a084",
"keywords": [
"Computer Aided Instruction",
"Serious Games Computing",
"Educational Domain",
"Gamification Experts",
"Education Experts",
"Confirmed Taxonomy",
"Gamification Strategies",
"Educational Contexts",
"Storytelling Game Elements",
"Narrative Game Elements",
"Games",
"Taxonomy",
"Education",
"Semantics",
"Terminology",
"Focusing",
"Proposals",
"Gamification",
"Education",
"Taxonomy",
"Experts",
"Survey"
],
"authors": [
{
"affiliation": "Durham University Institute of Mathematics and Computer Science, University of Sao Paulo",
"fullName": "Armando M. Toda",
"givenName": "Armando M.",
"surname": "Toda",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute of Mathematics and Computer Science, University of Sao Paulo",
"fullName": "Wilk Oliveira",
"givenName": "Wilk",
"surname": "Oliveira",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Federal University of Rio Grande do Sul",
"fullName": "Ana C. Klock",
"givenName": "Ana C.",
"surname": "Klock",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute of Mathematics and Computer Science, University of Sao Paulo",
"fullName": "Paula T. Palomino",
"givenName": "Paula T.",
"surname": "Palomino",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Federal University of Rio Grande do Sul",
"fullName": "Marcelo Pimenta",
"givenName": "Marcelo",
"surname": "Pimenta",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Santa Catarina State University",
"fullName": "Isabela Gasparini",
"givenName": "Isabela",
"surname": "Gasparini",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Liverpool",
"fullName": "Lei Shi",
"givenName": "Lei",
"surname": "Shi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Federal University of Alagoas",
"fullName": "Ig Bittencourt",
"givenName": "Ig",
"surname": "Bittencourt",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute of Mathematics and Computer Science, University of Sao Paulo",
"fullName": "Seiji Isotani",
"givenName": "Seiji",
"surname": "Isotani",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Durham University",
"fullName": "Alexandra I. Cristea",
"givenName": "Alexandra I.",
"surname": "Cristea",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icalt",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-07-01T00:00:00",
"pubType": "proceedings",
"pages": "84-88",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-3485-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "348500a081",
"articleId": "1cYi176wLiE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "348500a089",
"articleId": "1cYi0iJin6M",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ucc/2014/7881/0/7881a603",
"title": "Tools for Gamification Analytics: A Survey",
"doi": null,
"abstractUrl": "/proceedings-article/ucc/2014/7881a603/12OmNANkofV",
"parentPublication": {
"id": "proceedings/ucc/2014/7881/0",
"title": "2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/svr/2017/3588/0/3588a271",
"title": "Gamification as a Learning Strategy in a Simulation of Dental Anesthesia",
"doi": null,
"abstractUrl": "/proceedings-article/svr/2017/3588a271/12OmNBgQFKa",
"parentPublication": {
"id": "proceedings/svr/2017/3588/0",
"title": "2017 19th Symposium on Virtual and Augmented Reality (SVR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cedem/2016/1042/0/07781903",
"title": "A Review of Introducing Game Elements to e-Participation",
"doi": null,
"abstractUrl": "/proceedings-article/cedem/2016/07781903/12OmNwpoFEw",
"parentPublication": {
"id": "proceedings/cedem/2016/1042/0",
"title": "2016 Conference for E-Democracy and Open Government (CeDEM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dui/2016/0842/0/07460062",
"title": "Supporting computational thinking through gamification",
"doi": null,
"abstractUrl": "/proceedings-article/3dui/2016/07460062/12OmNzvQI3S",
"parentPublication": {
"id": "proceedings/3dui/2016/0842/0",
"title": "2016 IEEE Symposium on 3D User Interfaces (3DUI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ucc/2013/5152/0/06809455",
"title": "GaML - A Modeling Language for Gamification",
"doi": null,
"abstractUrl": "/proceedings-article/ucc/2013/06809455/12OmNzwZ6ut",
"parentPublication": {
"id": "proceedings/ucc/2013/5152/0",
"title": "2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vs-games/2019/4540/0/08864535",
"title": "A product to gamify other products; implementing gamification in existing software",
"doi": null,
"abstractUrl": "/proceedings-article/vs-games/2019/08864535/1e5ZqeAttAY",
"parentPublication": {
"id": "proceedings/vs-games/2019/4540/0",
"title": "2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2020/6090/0/09156051",
"title": "Gamification in online discussions: How do game elements affect critical thinking?",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2020/09156051/1m1j7Gl7WsU",
"parentPublication": {
"id": "proceedings/icalt/2020/6090/0",
"title": "2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbms/2020/9429/0/942900a138",
"title": "A Gamification-Based Framework for mHealth Developers in the Context of Self-Care",
"doi": null,
"abstractUrl": "/proceedings-article/cbms/2020/942900a138/1mLMkTh1tuw",
"parentPublication": {
"id": "proceedings/cbms/2020/9429/0",
"title": "2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2020/8961/0/09274194",
"title": "A teaching proposal for the software measurement process using gamification: an experimental study",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2020/09274194/1phRE77bKr6",
"parentPublication": {
"id": "proceedings/fie/2020/8961/0",
"title": "2020 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/seaa/2021/2705/0/270500a017",
"title": "A Systematic Mapping of Negative Effects of Gamification in Education/Learning Systems",
"doi": null,
"abstractUrl": "/proceedings-article/seaa/2021/270500a017/1y2JCgebKA8",
"parentPublication": {
"id": "proceedings/seaa/2021/2705/0",
"title": "2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNxWcH18",
"title": "2014 International Conference on Cyberworlds (CW)",
"acronym": "cw",
"groupId": "1000175",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBaBuQH",
"doi": "10.1109/CW.2014.18",
"title": "Feasibility Study for Contemporary Dance E-Learning: An Interactive Creation Support System Using 3D Motion Data",
"normalizedTitle": "Feasibility Study for Contemporary Dance E-Learning: An Interactive Creation Support System Using 3D Motion Data",
"abstract": "This paper describes a new method of dance e-learning by which students can learn to create contemporary dance choreography using 3D motion data. The method is designed to promote discovery learning of contemporary dance. \"Body-part Motion Synthesis System (BMSS) \" is implemented on tablets, which allows users to select body-part motion clips of basic dance movement and preview a short dance sequence synthesizing them by 3DCG in real time. Body-part motions of contemporary dance performed by a professional dancer are captured and prepared as 3D motion clips. To evaluate the feasibility of the learning effects of the e-learning method using BMSS two evaluation experiments were conducted. In the first experiment, 18 students who majored in contemporary dance at universities in Japan and the U.S. Created short dance pieces using the system and performed them by themselves. In the second experiment, five Japanese dance critics evaluated the video of their performances comparing 3DCG animation. As a consequence of the experiments, we verified that our e-learning method for contemporary dance is effective as a trigger of discovery learning to find both a new choreographic method and original dance movements.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper describes a new method of dance e-learning by which students can learn to create contemporary dance choreography using 3D motion data. The method is designed to promote discovery learning of contemporary dance. \"Body-part Motion Synthesis System (BMSS) \" is implemented on tablets, which allows users to select body-part motion clips of basic dance movement and preview a short dance sequence synthesizing them by 3DCG in real time. Body-part motions of contemporary dance performed by a professional dancer are captured and prepared as 3D motion clips. To evaluate the feasibility of the learning effects of the e-learning method using BMSS two evaluation experiments were conducted. In the first experiment, 18 students who majored in contemporary dance at universities in Japan and the U.S. Created short dance pieces using the system and performed them by themselves. In the second experiment, five Japanese dance critics evaluated the video of their performances comparing 3DCG animation. As a consequence of the experiments, we verified that our e-learning method for contemporary dance is effective as a trigger of discovery learning to find both a new choreographic method and original dance movements.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper describes a new method of dance e-learning by which students can learn to create contemporary dance choreography using 3D motion data. The method is designed to promote discovery learning of contemporary dance. \"Body-part Motion Synthesis System (BMSS) \" is implemented on tablets, which allows users to select body-part motion clips of basic dance movement and preview a short dance sequence synthesizing them by 3DCG in real time. Body-part motions of contemporary dance performed by a professional dancer are captured and prepared as 3D motion clips. To evaluate the feasibility of the learning effects of the e-learning method using BMSS two evaluation experiments were conducted. In the first experiment, 18 students who majored in contemporary dance at universities in Japan and the U.S. Created short dance pieces using the system and performed them by themselves. In the second experiment, five Japanese dance critics evaluated the video of their performances comparing 3DCG animation. As a consequence of the experiments, we verified that our e-learning method for contemporary dance is effective as a trigger of discovery learning to find both a new choreographic method and original dance movements.",
"fno": "4677a071",
"keywords": [
"Educational Institutions",
"Animation",
"Graphical User Interfaces",
"Electronic Learning",
"Three Dimensional Displays",
"Hip",
"Neck",
"3 DCG",
"Motion Data",
"E Learning",
"Dance"
],
"authors": [
{
"affiliation": null,
"fullName": "Bin Umino",
"givenName": "Bin",
"surname": "Umino",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Asako Soga",
"givenName": "Asako",
"surname": "Soga",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Motoko Hirayama",
"givenName": "Motoko",
"surname": "Hirayama",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2014-10-01T00:00:00",
"pubType": "proceedings",
"pages": "71-76",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-4677-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4677a063",
"articleId": "12OmNBlXs1W",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4677a077",
"articleId": "12OmNAsBFMG",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cw/2009/3791/0/3791a171",
"title": "Automatic Composition for Contemporary Dance Using 3D Motion Clips: Experiment on Dance Training and System Evaluation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2009/3791a171/12OmNwEJ0HF",
"parentPublication": {
"id": "proceedings/cw/2009/3791/0",
"title": "2009 International Conference on CyberWorlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2012/4814/0/4814a045",
"title": "Development of Easy-to-Use Authoring System for Noh (Japanese Traditional) Dance Animation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2012/4814a045/12OmNxaNGmE",
"parentPublication": {
"id": "proceedings/cw/2012/4814/0",
"title": "2012 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2015/9403/0/9403a200",
"title": "Automatic Composition by Body-Part Motion Synthesis for Supporting Dance Creation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2015/9403a200/12OmNyOq55Y",
"parentPublication": {
"id": "proceedings/cw/2015/9403/0",
"title": "2015 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/03/ttg2012030501",
"title": "Example-Based Automatic Music-Driven Conventional Dance Motion Synthesis",
"doi": null,
"abstractUrl": "/journal/tg/2012/03/ttg2012030501/13rRUwwaKt6",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/culture-and-computing/2017/1135/0/08227345",
"title": "Analysis of Interpersonal Effects in Dance Performance",
"doi": null,
"abstractUrl": "/proceedings-article/culture-and-computing/2017/08227345/17D45VObpPv",
"parentPublication": {
"id": "proceedings/culture-and-computing/2017/1135/0",
"title": "2017 International Conference on Culture and Computing (Culture and Computing)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nicoint/2022/6908/0/690800a104",
"title": "Prototype System of Dance Movement Creation by VR Experience of Augmented Human Body",
"doi": null,
"abstractUrl": "/proceedings-article/nicoint/2022/690800a104/1FWn0mibOE0",
"parentPublication": {
"id": "proceedings/nicoint/2022/6908/0",
"title": "2022 Nicograph International (NicoInt)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/10018173",
"title": "Keyframe Control of Music-driven 3D Dance Generation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/10018173/1JYZ6TXyjgk",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vs-games/2019/4540/0/08864530",
"title": "Immersive Simulation and Training of Person-to-3D Character Dance in Real-Time",
"doi": null,
"abstractUrl": "/proceedings-article/vs-games/2019/08864530/1e5ZqMvxwiY",
"parentPublication": {
"id": "proceedings/vs-games/2019/4540/0",
"title": "2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2020/6497/0/649700a117",
"title": "Experimental Creation of Dance by Professional Choreographers Using a Body-part Motion Synthesis System",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2020/649700a117/1olHz4Y8shO",
"parentPublication": {
"id": "proceedings/cw/2020/6497/0",
"title": "2020 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eimss/2021/2707/0/270700a022",
"title": "Research on the Application of Digital Media Technology in Sports Dance Teaching",
"doi": null,
"abstractUrl": "/proceedings-article/eimss/2021/270700a022/1yEZQHwiT6w",
"parentPublication": {
"id": "proceedings/eimss/2021/2707/0",
"title": "2021 International Conference on Education, Information Management and Service Science (EIMSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNBubORS",
"title": "2015 International Conference on Cyberworlds (CW)",
"acronym": "cw",
"groupId": "1000175",
"volume": "0",
"displayVolume": "0",
"year": "2015",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNyOq55Y",
"doi": "10.1109/CW.2015.26",
"title": "Automatic Composition by Body-Part Motion Synthesis for Supporting Dance Creation",
"normalizedTitle": "Automatic Composition by Body-Part Motion Synthesis for Supporting Dance Creation",
"abstract": "This paper describes a system for supporting the creation of contemporary dance choreography using 3D motion data acquired by motion capture. We developed a system that automatically generates short choreographies by combining a basic motion with multiple body-part motions. The generated choreographies are simulated in 3D animation. It runs on a tablet, so users can select a basic motion and body-part categories by touch operations. The motions are selected and synthesized to the base motion at random timing. To reduce the partial motion synthesis and increase the variety of the generated choreographies, the synthesis timing is adjusted. We experimentally evaluated the effectiveness of our system with eight dancers. From questionnaire results gathered after the experiment, we received many comments about the effectiveness for creation of dance, training of dance techniques, and understanding of dance movements.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper describes a system for supporting the creation of contemporary dance choreography using 3D motion data acquired by motion capture. We developed a system that automatically generates short choreographies by combining a basic motion with multiple body-part motions. The generated choreographies are simulated in 3D animation. It runs on a tablet, so users can select a basic motion and body-part categories by touch operations. The motions are selected and synthesized to the base motion at random timing. To reduce the partial motion synthesis and increase the variety of the generated choreographies, the synthesis timing is adjusted. We experimentally evaluated the effectiveness of our system with eight dancers. From questionnaire results gathered after the experiment, we received many comments about the effectiveness for creation of dance, training of dance techniques, and understanding of dance movements.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper describes a system for supporting the creation of contemporary dance choreography using 3D motion data acquired by motion capture. We developed a system that automatically generates short choreographies by combining a basic motion with multiple body-part motions. The generated choreographies are simulated in 3D animation. It runs on a tablet, so users can select a basic motion and body-part categories by touch operations. The motions are selected and synthesized to the base motion at random timing. To reduce the partial motion synthesis and increase the variety of the generated choreographies, the synthesis timing is adjusted. We experimentally evaluated the effectiveness of our system with eight dancers. From questionnaire results gathered after the experiment, we received many comments about the effectiveness for creation of dance, training of dance techniques, and understanding of dance movements.",
"fno": "9403a200",
"keywords": [
"Timing",
"Training",
"Animation",
"Three Dimensional Displays",
"Cameras",
"Neck",
"Floors",
"Choreography",
"Motion Data",
"Dance",
"Automatic Composition",
"Motion Synthesis"
],
"authors": [
{
"affiliation": null,
"fullName": "Yuho Yazaki",
"givenName": "Yuho",
"surname": "Yazaki",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Asako Soga",
"givenName": "Asako",
"surname": "Soga",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Bin Umino",
"givenName": "Bin",
"surname": "Umino",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Motoko Hirayama",
"givenName": "Motoko",
"surname": "Hirayama",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2015-10-01T00:00:00",
"pubType": "proceedings",
"pages": "200-203",
"year": "2015",
"issn": null,
"isbn": "978-1-4673-9403-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "9403a193",
"articleId": "12OmNyGtjdR",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "9403a204",
"articleId": "12OmNAOKnSy",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cw/2006/2671/0/26710043",
"title": "Automatic Composition and Simulation System for Ballet Sequences",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2006/26710043/12OmNAlvHMT",
"parentPublication": {
"id": "proceedings/cw/2006/2671/0",
"title": "2006 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2014/4677/0/4677a071",
"title": "Feasibility Study for Contemporary Dance E-Learning: An Interactive Creation Support System Using 3D Motion Data",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2014/4677a071/12OmNBaBuQH",
"parentPublication": {
"id": "proceedings/cw/2014/4677/0",
"title": "2014 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2009/3791/0/3791a171",
"title": "Automatic Composition for Contemporary Dance Using 3D Motion Clips: Experiment on Dance Training and System Evaluation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2009/3791a171/12OmNwEJ0HF",
"parentPublication": {
"id": "proceedings/cw/2009/3791/0",
"title": "2009 International Conference on CyberWorlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2012/4814/0/4814a045",
"title": "Development of Easy-to-Use Authoring System for Noh (Japanese Traditional) Dance Animation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2012/4814a045/12OmNxaNGmE",
"parentPublication": {
"id": "proceedings/cw/2012/4814/0",
"title": "2012 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2014/4677/0/4677a253",
"title": "Sketch-Based Dance Choreography",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2014/4677a253/12OmNzaQoBR",
"parentPublication": {
"id": "proceedings/cw/2014/4677/0",
"title": "2014 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/03/ttg2012030501",
"title": "Example-Based Automatic Music-Driven Conventional Dance Motion Synthesis",
"doi": null,
"abstractUrl": "/journal/tg/2012/03/ttg2012030501/13rRUwwaKt6",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2017/06/mcg2017060005",
"title": "Computer Graphics Animation for Objective Self-Evaluation",
"doi": null,
"abstractUrl": "/magazine/cg/2017/06/mcg2017060005/13rRUy3gn3D",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nicoint/2022/6908/0/690800a104",
"title": "Prototype System of Dance Movement Creation by VR Experience of Augmented Human Body",
"doi": null,
"abstractUrl": "/proceedings-article/nicoint/2022/690800a104/1FWn0mibOE0",
"parentPublication": {
"id": "proceedings/nicoint/2022/6908/0",
"title": "2022 Nicograph International (NicoInt)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/10018173",
"title": "Keyframe Control of Music-driven 3D Dance Generation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/10018173/1JYZ6TXyjgk",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2020/6497/0/649700a117",
"title": "Experimental Creation of Dance by Professional Choreographers Using a Body-part Motion Synthesis System",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2020/649700a117/1olHz4Y8shO",
"parentPublication": {
"id": "proceedings/cw/2020/6497/0",
"title": "2020 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1G4EUUmGcrS",
"title": "2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)",
"acronym": "icmew",
"groupId": "1801805",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1G4EWcJ9cCk",
"doi": "10.1109/ICMEW56448.2022.9859441",
"title": "DAMUS: A Collaborative System for Choreography and Music Composition",
"normalizedTitle": "DAMUS: A Collaborative System for Choreography and Music Composition",
"abstract": "Throughout the history of dance and music collaborations, composers and choreographers have always engaged in separate workflows. Usually, composers and choreographers complete the music and choreograph the moves separately, where the lack of mutual understanding of their artistic approaches results in a long production time. There is a strong need in the performance industry to reduce the time for establishing a collaborative foundation, allowing for more productive creations. We propose DAMUS, a work-in-progress collaborative system for choreography and music composition, in order to reduce production time and boost productivity.DAMUS is composed of a dance module DA and a music module MUS. DA translates dance motion into MoCap data, Labanotation, and number notation, and sets rules of variations for choreography. MUS produces musical materials that fit the tempo and rhythm of specific dance genres or moves. We applied our system prototype to case studies in three different genres. In the future, we plan to pursue more genres and further develop DAMUS with evolutionary computation and style transfer.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Throughout the history of dance and music collaborations, composers and choreographers have always engaged in separate workflows. Usually, composers and choreographers complete the music and choreograph the moves separately, where the lack of mutual understanding of their artistic approaches results in a long production time. There is a strong need in the performance industry to reduce the time for establishing a collaborative foundation, allowing for more productive creations. We propose DAMUS, a work-in-progress collaborative system for choreography and music composition, in order to reduce production time and boost productivity.DAMUS is composed of a dance module DA and a music module MUS. DA translates dance motion into MoCap data, Labanotation, and number notation, and sets rules of variations for choreography. MUS produces musical materials that fit the tempo and rhythm of specific dance genres or moves. We applied our system prototype to case studies in three different genres. In the future, we plan to pursue more genres and further develop DAMUS with evolutionary computation and style transfer.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Throughout the history of dance and music collaborations, composers and choreographers have always engaged in separate workflows. Usually, composers and choreographers complete the music and choreograph the moves separately, where the lack of mutual understanding of their artistic approaches results in a long production time. There is a strong need in the performance industry to reduce the time for establishing a collaborative foundation, allowing for more productive creations. We propose DAMUS, a work-in-progress collaborative system for choreography and music composition, in order to reduce production time and boost productivity.DAMUS is composed of a dance module DA and a music module MUS. DA translates dance motion into MoCap data, Labanotation, and number notation, and sets rules of variations for choreography. MUS produces musical materials that fit the tempo and rhythm of specific dance genres or moves. We applied our system prototype to case studies in three different genres. In the future, we plan to pursue more genres and further develop DAMUS with evolutionary computation and style transfer.",
"fno": "09859441",
"keywords": [
"Evolutionary Computation",
"Humanities",
"Image Motion Analysis",
"Music",
"DAMUS",
"Choreography",
"Music Composition",
"Music Collaborations",
"Composers",
"Separate Workflows",
"Choreographers Complete The Music",
"Mutual Understanding",
"Artistic Approaches Results",
"Long Production Time",
"Performance Industry",
"Collaborative Foundation",
"Productive Creations",
"Work In Progress Collaborative System",
"Boost Productivity",
"Dance Module DA",
"Music Module MUS",
"Musical Materials",
"Specific Dance Genres",
"System Prototype",
"Productivity",
"Industries",
"Multimedia Systems",
"Conferences",
"Collaboration",
"Prototypes",
"Evolutionary Computation",
"Choreography",
"Music Composition",
"Collaborative System"
],
"authors": [
{
"affiliation": "Beijing Normal University,School of Future Design",
"fullName": "Tiange Zhou",
"givenName": "Tiange",
"surname": "Zhou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Normal University,School of Future Design",
"fullName": "Borou Yu",
"givenName": "Borou",
"surname": "Yu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Normal University,School of Future Design",
"fullName": "Jiajian Min",
"givenName": "Jiajian",
"surname": "Min",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Normal University,School of Future Design",
"fullName": "Zeyu Wang",
"givenName": "Zeyu",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icmew",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-07-01T00:00:00",
"pubType": "proceedings",
"pages": "1-6",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-7218-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09859330",
"articleId": "1G4EVZOXYWs",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09859363",
"articleId": "1G4F0ChV0Ji",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cw/2006/2671/0/26710043",
"title": "Automatic Composition and Simulation System for Ballet Sequences",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2006/26710043/12OmNAlvHMT",
"parentPublication": {
"id": "proceedings/cw/2006/2671/0",
"title": "2006 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/culture-computing/2013/5047/0/5047a155",
"title": "Resynchronize Japanese \"Geisha\" Dance Video Using Music of Different Styles",
"doi": null,
"abstractUrl": "/proceedings-article/culture-computing/2013/5047a155/12OmNB0nWdt",
"parentPublication": {
"id": "proceedings/culture-computing/2013/5047/0",
"title": "2013 International Conference on Culture and Computing (Culture Computing)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2008/2570/0/04607692",
"title": "A study of image-based music composition",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2008/04607692/12OmNvT2p19",
"parentPublication": {
"id": "proceedings/icme/2008/2570/0",
"title": "2008 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dexa/2001/1230/0/12300537",
"title": "A System for Collaborative Music Composition over the Web",
"doi": null,
"abstractUrl": "/proceedings-article/dexa/2001/12300537/12OmNyL0TF6",
"parentPublication": {
"id": "proceedings/dexa/2001/1230/0",
"title": "12th International Workshop on Database and Expert Systems Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isspit/2014/1812/0/07300559",
"title": "Intelligent classification of electronic music",
"doi": null,
"abstractUrl": "/proceedings-article/isspit/2014/07300559/12OmNzvQHWJ",
"parentPublication": {
"id": "proceedings/isspit/2014/1812/0",
"title": "2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/03/ttg2012030501",
"title": "Example-Based Automatic Music-Driven Conventional Dance Motion Synthesis",
"doi": null,
"abstractUrl": "/journal/tg/2012/03/ttg2012030501/13rRUwwaKt6",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200n3381",
"title": "AI Choreographer: Music Conditioned 3D Dance Generation with AIST++",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200n3381/1BmJ1TiWSB2",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600d480",
"title": "A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600d480/1H1lISb1OjS",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/synasc/2018/0625/0/062500a253",
"title": "Genetic Operators in Evolutionary Music Composition",
"doi": null,
"abstractUrl": "/proceedings-article/synasc/2018/062500a253/1bhJulfCc2Q",
"parentPublication": {
"id": "proceedings/synasc/2018/0625/0",
"title": "2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mipr/2021/1865/0/186500a348",
"title": "Dance to Music: Generative Choreography with Music using Mixture Density Networks",
"doi": null,
"abstractUrl": "/proceedings-article/mipr/2021/186500a348/1xPslGYA8Gk",
"parentPublication": {
"id": "proceedings/mipr/2021/1865/0",
"title": "2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "19m3yLbYQdq",
"title": "2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)",
"acronym": "iiai-aai",
"groupId": "1801921",
"volume": "0",
"displayVolume": "0",
"year": "2018",
"__typename": "ProceedingType"
},
"article": {
"id": "19m3G4aWrSM",
"doi": "10.1109/IIAI-AAI.2018.00177",
"title": "Study on Aesthetic Impression of Female Body Type and Posture",
"normalizedTitle": "Study on Aesthetic Impression of Female Body Type and Posture",
"abstract": "The purpose of this research is to clarify the relationship between the female body type and posture and the aesthetic impression it creates. The criterion of judgment of the beauty of the female body was based on the degree of body mass (BMI), the degree of bust, waist and hips, but also evaluated the impression added by posture. We divided the BMI into 5 levels, body types into 3 levels, and posture into 6 types, making sample images created using a 3D body scanner. In the research part 1, we used 40 samples that emphasized the body line, and in part 2 we evaluated using 19 samples assuming loose clothing. Questionnaires were conducted with adult women using the SD method with 19 adjective pairs. As a result of computing the total of the scores and the principal component analysis, the first principal component axis showed \"beauty, attractiveness\" and the second principal component axis shows \"plumpness, slimness\". Regardless of the sharpness of the body line, the weak upright type had the lowest score for positive impression. The balanced upright type was considered to be beautiful. The impression of the form of women showed that the elements of posture are related to beauty.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The purpose of this research is to clarify the relationship between the female body type and posture and the aesthetic impression it creates. The criterion of judgment of the beauty of the female body was based on the degree of body mass (BMI), the degree of bust, waist and hips, but also evaluated the impression added by posture. We divided the BMI into 5 levels, body types into 3 levels, and posture into 6 types, making sample images created using a 3D body scanner. In the research part 1, we used 40 samples that emphasized the body line, and in part 2 we evaluated using 19 samples assuming loose clothing. Questionnaires were conducted with adult women using the SD method with 19 adjective pairs. As a result of computing the total of the scores and the principal component analysis, the first principal component axis showed \"beauty, attractiveness\" and the second principal component axis shows \"plumpness, slimness\". Regardless of the sharpness of the body line, the weak upright type had the lowest score for positive impression. The balanced upright type was considered to be beautiful. The impression of the form of women showed that the elements of posture are related to beauty.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The purpose of this research is to clarify the relationship between the female body type and posture and the aesthetic impression it creates. The criterion of judgment of the beauty of the female body was based on the degree of body mass (BMI), the degree of bust, waist and hips, but also evaluated the impression added by posture. We divided the BMI into 5 levels, body types into 3 levels, and posture into 6 types, making sample images created using a 3D body scanner. In the research part 1, we used 40 samples that emphasized the body line, and in part 2 we evaluated using 19 samples assuming loose clothing. Questionnaires were conducted with adult women using the SD method with 19 adjective pairs. As a result of computing the total of the scores and the principal component analysis, the first principal component axis showed \"beauty, attractiveness\" and the second principal component axis shows \"plumpness, slimness\". Regardless of the sharpness of the body line, the weak upright type had the lowest score for positive impression. The balanced upright type was considered to be beautiful. The impression of the form of women showed that the elements of posture are related to beauty.",
"fno": "744701a873",
"keywords": [
"Image Sampling",
"Image Sensors",
"Principal Component Analysis",
"Posture",
"BMI",
"3 D Body Scanner",
"Body Line",
"Principal Component Axis",
"Weak Upright Type",
"Positive Impression",
"Balanced Upright Type",
"Aesthetic Impression",
"Female Body Type",
"Body Mass",
"Principal Component Analysis",
"Degree Of Body Mass",
"SD Method",
"Shape",
"Principal Component Analysis",
"Three Dimensional Displays",
"Standards",
"Hip",
"Shape Measurement",
"Clothing",
"Body Type",
"BMI",
"Posture",
"Aesthetic Impression",
"Principal Component Analysis"
],
"authors": [
{
"affiliation": "Waseda Univ., Saitama, Japan",
"fullName": "Masami Miyazaki",
"givenName": "Masami",
"surname": "Miyazaki",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Waseda Univ., Saitama, Japan",
"fullName": "Toru Sugahara",
"givenName": "Toru",
"surname": "Sugahara",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Waseda Univ., Saitama, Japan",
"fullName": "Ryuma Onose",
"givenName": "Ryuma",
"surname": "Onose",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Style Presenter, Tokyo, Japan",
"fullName": "Yuko Yamaguchi",
"givenName": "Yuko",
"surname": "Yamaguchi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Wacoal Human Sci. Res. Center, Kyoto, Japan",
"fullName": "Taizo Kishimoto",
"givenName": "Taizo",
"surname": "Kishimoto",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Wacoal Human Sci. Res. Center, Kyoto, Japan",
"fullName": "Ueke Satoko",
"givenName": "Ueke",
"surname": "Satoko",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Wacoal Human Sci. Res. Center, Kyoto, Japan",
"fullName": "Hiroma Kurono",
"givenName": "Hiroma",
"surname": "Kurono",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iiai-aai",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2018-07-01T00:00:00",
"pubType": "proceedings",
"pages": "873-876",
"year": "2018",
"issn": null,
"isbn": "978-1-5386-7447-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "744701a867",
"articleId": "19m3DWfIWPK",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "744701a877",
"articleId": "19m3zlb3YAw",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icfcse/2011/1562/0/06041692",
"title": "A Study about the Evaluation of Beauty of the Body Type of Female Students in College",
"doi": null,
"abstractUrl": "/proceedings-article/icfcse/2011/06041692/12OmNA14A6C",
"parentPublication": {
"id": "proceedings/icfcse/2011/1562/0",
"title": "2011 International Conference on Future Computer Science and Education",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/caapwd/1992/2730/0/00217386",
"title": "The Posture Monitor: an automated prompting device for body alignment",
"doi": null,
"abstractUrl": "/proceedings-article/caapwd/1992/00217386/12OmNBhpRXj",
"parentPublication": {
"id": "proceedings/caapwd/1992/2730/0",
"title": "Proceedings of the Johns Hopkins National Search for Computing Applications to Assist Persons with Disabilities",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/motion/2002/1860/0/18600175",
"title": "Ghost3D: Detecting Body Posture and Parts Using Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/motion/2002/18600175/12OmNqHIttW",
"parentPublication": {
"id": "proceedings/motion/2002/1860/0",
"title": "Proceedings Workshop on Motion and Video Computing (MOTION 2002)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2017/6067/0/08019410",
"title": "Weakly structured information aggregation for upper-body posture assessment using ConvNets",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2017/08019410/12OmNvAiSho",
"parentPublication": {
"id": "proceedings/icme/2017/6067/0",
"title": "2017 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmew/2014/4717/0/06890654",
"title": "A convenient photo-based approach for assessing body posture",
"doi": null,
"abstractUrl": "/proceedings-article/icmew/2014/06890654/12OmNxUMHnr",
"parentPublication": {
"id": "proceedings/icmew/2014/4717/0",
"title": "2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2013/5089/0/5089a376",
"title": "Image-Based Fall Detection with Human Posture Sequence Modeling",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2013/5089a376/12OmNyr8Ypb",
"parentPublication": {
"id": "proceedings/ichi/2013/5089/0",
"title": "2013 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dpvt/2002/1521/0/15210636",
"title": "Posture Recognition nd Segmentation from 3D Human Body Scans",
"doi": null,
"abstractUrl": "/proceedings-article/3dpvt/2002/15210636/12OmNzBOhIz",
"parentPublication": {
"id": "proceedings/3dpvt/2002/1521/0",
"title": "3D Data Processing Visualization and Transmission, International Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2002/1695/1/169510319",
"title": "Wavelet Moments for Recognizing Human Body Posture from 3D Scans",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2002/169510319/12OmNzICENZ",
"parentPublication": {
"id": "proceedings/icpr/2002/1695/1",
"title": "Proceedings of 16th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2008/2174/0/04761608",
"title": "3D human posture estimation using the HOG features from monocular image",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2008/04761608/12OmNzaQoJr",
"parentPublication": {
"id": "proceedings/icpr/2008/2174/0",
"title": "ICPR 2008 19th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09744001",
"title": "Influence of user posture and virtual exercise on impression of locomotion during VR observation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09744001/1C8BFV420lq",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1J2XJb8ZJ9C",
"title": "2022 Topological Data Analysis and Visualization (TopoInVis)",
"acronym": "topoinvis",
"groupId": "1848466",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1J2XLcCgpVK",
"doi": "10.1109/TopoInVis57755.2022.00018",
"title": "Subject-Specific Brain Activity Analysis in fMRI Data Using Merge Trees",
"normalizedTitle": "Subject-Specific Brain Activity Analysis in fMRI Data Using Merge Trees",
"abstract": "We present a method for detecting patterns in time-varying functional magnetic resonance imaging (fMRI) data based on topological analysis. The oxygenated blood flow measured by fMRI is widely used as an indicator of brain activity. The signal is, however, prone to noise from various sources. Random brain activity, physiological noise, and noise from the scanner can reach a strength comparable to the signal itself. Thus, extracting the underlying signal is a challenging process typically approached by applying statistical methods. The goal of this work is to investigate the possibilities of recovering information from the signal using topological feature vectors directly based on the raw signal without medical domain priors. We utilize merge trees to define a robust feature vector capturing key features within a time step of fMRI data. We demonstrate how such a concise feature vector representation can be utilized for exploring the temporal development of brain activations, connectivity between these activations, and their relation to cognitive tasks.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a method for detecting patterns in time-varying functional magnetic resonance imaging (fMRI) data based on topological analysis. The oxygenated blood flow measured by fMRI is widely used as an indicator of brain activity. The signal is, however, prone to noise from various sources. Random brain activity, physiological noise, and noise from the scanner can reach a strength comparable to the signal itself. Thus, extracting the underlying signal is a challenging process typically approached by applying statistical methods. The goal of this work is to investigate the possibilities of recovering information from the signal using topological feature vectors directly based on the raw signal without medical domain priors. We utilize merge trees to define a robust feature vector capturing key features within a time step of fMRI data. We demonstrate how such a concise feature vector representation can be utilized for exploring the temporal development of brain activations, connectivity between these activations, and their relation to cognitive tasks.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present a method for detecting patterns in time-varying functional magnetic resonance imaging (fMRI) data based on topological analysis. The oxygenated blood flow measured by fMRI is widely used as an indicator of brain activity. The signal is, however, prone to noise from various sources. Random brain activity, physiological noise, and noise from the scanner can reach a strength comparable to the signal itself. Thus, extracting the underlying signal is a challenging process typically approached by applying statistical methods. The goal of this work is to investigate the possibilities of recovering information from the signal using topological feature vectors directly based on the raw signal without medical domain priors. We utilize merge trees to define a robust feature vector capturing key features within a time step of fMRI data. We demonstrate how such a concise feature vector representation can be utilized for exploring the temporal development of brain activations, connectivity between these activations, and their relation to cognitive tasks.",
"fno": "935400a113",
"keywords": [
"Biomedical MRI",
"Brain",
"Cognition",
"Feature Extraction",
"Haemodynamics",
"Medical Image Processing",
"Brain Activity Analysis",
"Cognitive Tasks",
"Feature Extraction",
"Feature Vector Representation",
"F MRI Data",
"Functional Magnetic Resonance Imaging Data",
"Merge Trees",
"Oxygenated Blood Flow",
"Physiological Noise",
"Human Computer Interaction",
"Brain",
"Data Analysis",
"Statistical Analysis",
"Feature Detection",
"Data Visualization",
"Functional Magnetic Resonance Imaging",
"F MRI Data Analysis",
"Data Abstraction",
"Temporal Data",
"Feature Detection",
"Merge Tree",
"Computational Topology Based Techniques"
],
"authors": [
{
"affiliation": "Linköping University",
"fullName": "Farhan Rasheed",
"givenName": "Farhan",
"surname": "Rasheed",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Linköping University",
"fullName": "Daniel Jönsson",
"givenName": "Daniel",
"surname": "Jönsson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Linköping University",
"fullName": "Emma Nilsson",
"givenName": "Emma",
"surname": "Nilsson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Linköping University",
"fullName": "Talha Bin Masood",
"givenName": "Talha Bin",
"surname": "Masood",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Linköping University",
"fullName": "Ingrid Hotz",
"givenName": "Ingrid",
"surname": "Hotz",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "topoinvis",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-10-01T00:00:00",
"pubType": "proceedings",
"pages": "113-123",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-9354-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "935400a103",
"articleId": "1J2XL5dyDJu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "935400a125",
"articleId": "1J2XLyxV0YM",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2011/0394/0/05995651",
"title": "Generalized group sparse classifiers with application in fMRI brain decoding",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2011/05995651/12OmNwcUk3T",
"parentPublication": {
"id": "proceedings/cvpr/2011/0394/0",
"title": "CVPR 2011",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/lssa/2006/0277/0/04015817",
"title": "Identification of brain activity from fMRI data: comparison of three fractal scaling analyses",
"doi": null,
"abstractUrl": "/proceedings-article/lssa/2006/04015817/12OmNzsJ7wr",
"parentPublication": {
"id": "proceedings/lssa/2006/0277/0",
"title": "2006 IEEE/NLM Life Science Systems and Applications Workshop",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ssiai/2018/6568/0/08470346",
"title": "f-Sim: A quasi-realistic fMRI simulation toolbox using digital brain phantom and modeled noise",
"doi": null,
"abstractUrl": "/proceedings-article/ssiai/2018/08470346/13WBGNdYtfu",
"parentPublication": {
"id": "proceedings/ssiai/2018/6568/0",
"title": "2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2019/02/08307234",
"title": "A Distributed Computing Platform for fMRI Big Data Analytics",
"doi": null,
"abstractUrl": "/journal/bd/2019/02/08307234/13rRUwvT9li",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2018/9288/0/928800a292",
"title": "Functional Brain Areas Mapping in Patients with Glioma Based on Resting-State fMRI Data Decomposition",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2018/928800a292/18jXDUv2t1K",
"parentPublication": {
"id": "proceedings/icdmw/2018/9288/0",
"title": "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669849",
"title": "A Graph Attention Neural Network for Diagnosing ASD with fMRI Data",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669849/1A9W7E4keTm",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10020955",
"title": "Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract)",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10020955/1KfS1ySJjEc",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2020/6090/0/09155943",
"title": "Learning English as Foreign Language through Guided Writing Practice: An fMRI Study of Healthy Taiwanese Students",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2020/09155943/1m1j58qLf7G",
"parentPublication": {
"id": "proceedings/icalt/2020/6090/0",
"title": "2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09413047",
"title": "Estimating Static and Dynamic Brain Networks by Kulback-Leibler Divergence from fMRI Data",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09413047/1tmhK9HGu1a",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412101",
"title": "fMRI Brain Networks as Statistical Mechanical Ensembles",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412101/1tmj67qcsda",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzl3WWY",
"title": "2007 11th IEEE International Conference on Computer Vision",
"acronym": "iccv",
"groupId": "1000149",
"volume": "0",
"displayVolume": "0",
"year": "2007",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNC1oT5B",
"doi": "10.1109/ICCV.2007.4409112",
"title": "On the Extraction of Curve Skeletons using Gradient Vector Flow",
"normalizedTitle": "On the Extraction of Curve Skeletons using Gradient Vector Flow",
"abstract": "In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path between any two medial voxels in the shape is a curve skeleton. We have employed two different medialness functions; the Euclidean distance field and a variant of the magnitude of the gradient vector flow (GVF), resulting in two different energy functions. The first energy controls the identification of the shape topological nodes from which curve skeletons start, while the second one controls the extraction of curve skeletons. The accuracy and robustness of the proposed framework are validated both quantitatively and qualitatively against competing techniques as well as several 3D shapes of different complexity.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path between any two medial voxels in the shape is a curve skeleton. We have employed two different medialness functions; the Euclidean distance field and a variant of the magnitude of the gradient vector flow (GVF), resulting in two different energy functions. The first energy controls the identification of the shape topological nodes from which curve skeletons start, while the second one controls the extraction of curve skeletons. The accuracy and robustness of the proposed framework are validated both quantitatively and qualitatively against competing techniques as well as several 3D shapes of different complexity.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path between any two medial voxels in the shape is a curve skeleton. We have employed two different medialness functions; the Euclidean distance field and a variant of the magnitude of the gradient vector flow (GVF), resulting in two different energy functions. The first energy controls the identification of the shape topological nodes from which curve skeletons start, while the second one controls the extraction of curve skeletons. The accuracy and robustness of the proposed framework are validated both quantitatively and qualitatively against competing techniques as well as several 3D shapes of different complexity.",
"fno": "04409112",
"keywords": [],
"authors": [
{
"affiliation": "Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY 40292. msabry@cvip.uofl.edu",
"fullName": "M. Sabry Hassouna",
"givenName": "M. Sabry",
"surname": "Hassouna",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY 40292. farag@cvip.uofl.edu",
"fullName": "Aly A. Farag",
"givenName": "Aly A.",
"surname": "Farag",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2007-10-01T00:00:00",
"pubType": "proceedings",
"pages": "1-8",
"year": "2007",
"issn": null,
"isbn": "978-1-4244-1630-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "04409111",
"articleId": "12OmNwFidcg",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "04409113",
"articleId": "12OmNBNM94P",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvprw/2010/7029/0/05543279",
"title": "Straight skeletons for binary shapes",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2010/05543279/12OmNyUnEJa",
"parentPublication": {
"id": "proceedings/cvprw/2010/7029/0",
"title": "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iciap/2001/1183/0/11830495",
"title": "Representing Volumetric Vascular Structures Using Curve Skeletons",
"doi": null,
"abstractUrl": "/proceedings-article/iciap/2001/11830495/12OmNz6iOsk",
"parentPublication": {
"id": "proceedings/iciap/2001/1183/0",
"title": "Proceedings ICIAP 2001. 11th International Conference on Image Analysis and Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/11/ttg2012111891",
"title": "Reconstructing the Curve-Skeletons of 3D Shapes Using the Visual Hull",
"doi": null,
"abstractUrl": "/journal/tg/2012/11/ttg2012111891/13rRUwIF6dP",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2008/02/ttg2008020355",
"title": "Computing Multiscale Curve and Surface Skeletons of Genus 0 Shapes Using a Global Importance Measure",
"doi": null,
"abstractUrl": "/journal/tg/2008/02/ttg2008020355/13rRUwd9CFZ",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2009/12/ttp2009122257",
"title": "Variational Curve Skeletons Using Gradient Vector Flow",
"doi": null,
"abstractUrl": "/journal/tp/2009/12/ttp2009122257/13rRUwwsltS",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2008/04/ttg2008040926",
"title": "Curve-Skeleton Extraction Using Iterative Least Squares Optimization",
"doi": null,
"abstractUrl": "/journal/tg/2008/04/ttg2008040926/13rRUxASuGa",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/01/07066924",
"title": "An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization",
"doi": null,
"abstractUrl": "/journal/tp/2016/01/07066924/13rRUxASuNO",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09741325",
"title": "Robustly Extracting Concise 3D Curve Skeletons by Enhancing the Capture of Prominent Features",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09741325/1C0jdavrcC4",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2020/09/08664178",
"title": "Mass-Driven Topology-Aware Curve Skeleton Extraction from Incomplete Point Clouds",
"doi": null,
"abstractUrl": "/journal/tg/2020/09/08664178/1lRhomN6HMk",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/03/09173765",
"title": "A Simple and Stable Centeredness Measure for 3D Curve Skeleton Extraction",
"doi": null,
"abstractUrl": "/journal/tg/2022/03/09173765/1mts9gNKec0",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNrNh0uC",
"title": "2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission",
"acronym": "3dimpvt",
"groupId": "1800494",
"volume": "0",
"displayVolume": "0",
"year": "2012",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNqAU6yz",
"doi": "10.1109/3DIMPVT.2012.30",
"title": "An Adaptive Hierarchical Approach to the Extraction of High Resolution Medial Surfaces",
"normalizedTitle": "An Adaptive Hierarchical Approach to the Extraction of High Resolution Medial Surfaces",
"abstract": "We introduce a novel algorithm for medial surfaces extraction that is based on the density-corrected Hamiltonian analysis. The approach extracts the skeleton directly from a triangulated mesh and adopts an adaptive octree-based approach in which only skeletal voxels are refined to a lower level of the hierarchy, resulting in robust and accurate skeletons at extremely high resolution. The quality of the extracted medial surfaces is confirmed by an extensive set of experiments.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We introduce a novel algorithm for medial surfaces extraction that is based on the density-corrected Hamiltonian analysis. The approach extracts the skeleton directly from a triangulated mesh and adopts an adaptive octree-based approach in which only skeletal voxels are refined to a lower level of the hierarchy, resulting in robust and accurate skeletons at extremely high resolution. The quality of the extracted medial surfaces is confirmed by an extensive set of experiments.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We introduce a novel algorithm for medial surfaces extraction that is based on the density-corrected Hamiltonian analysis. The approach extracts the skeleton directly from a triangulated mesh and adopts an adaptive octree-based approach in which only skeletal voxels are refined to a lower level of the hierarchy, resulting in robust and accurate skeletons at extremely high resolution. The quality of the extracted medial surfaces is confirmed by an extensive set of experiments.",
"fno": "4873a371",
"keywords": [
"Hierarchical Skeleton",
"Medial Surface",
"Surface Skeleton"
],
"authors": [
{
"affiliation": null,
"fullName": "Luca Rossi",
"givenName": "Luca",
"surname": "Rossi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Andrea Torsello",
"givenName": "Andrea",
"surname": "Torsello",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "3dimpvt",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2012-10-01T00:00:00",
"pubType": "proceedings",
"pages": "371-378",
"year": "2012",
"issn": null,
"isbn": "978-1-4673-4470-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "4873a363",
"articleId": "12OmNAsBFIX",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "4873a379",
"articleId": "12OmNBOlllu",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/sibgrapi/2008/3358/0/3358a212",
"title": "New Higher-Resolution Discrete Euclidean Medial Axis in nD with Linear Time Parallel Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2008/3358a212/12OmNApu5fr",
"parentPublication": {
"id": "proceedings/sibgrapi/2008/3358/0",
"title": "2008 XXI Brazilian Symposium on Computer Graphics and Image Processing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icig/2011/4541/0/4541a182",
"title": "A Medial Axis Extraction Algorithm for the Processing of Combustion Flame Images",
"doi": null,
"abstractUrl": "/proceedings-article/icig/2011/4541a182/12OmNrYlmTY",
"parentPublication": {
"id": "proceedings/icig/2011/4541/0",
"title": "Image and Graphics, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/smi/2005/2379/0/23790228",
"title": "A Tracing Algorithm for Constructing Medial Axis Transform of 3D Objects Bound by Free-Form Surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/smi/2005/23790228/12OmNrkT7sh",
"parentPublication": {
"id": "proceedings/smi/2005/2379/0",
"title": "Proceedings. International Conference on Shape Modeling and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/smi/2005/2379/0/23790290",
"title": "Computational Topology for Reconstruction of Surfaces with Boundary: Integrating Experiments and Theory",
"doi": null,
"abstractUrl": "/proceedings-article/smi/2005/23790290/12OmNs59JNj",
"parentPublication": {
"id": "proceedings/smi/2005/2379/0",
"title": "Proceedings. International Conference on Shape Modeling and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2000/0750/1/07501712",
"title": "Object Representation and Comparison Inferred from Its Medial Axis",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2000/07501712/12OmNvDI45q",
"parentPublication": {
"id": "proceedings/icpr/2000/0750/1",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isvd/2006/2630/0/26300040",
"title": "Stable and Topology-Preserving Extraction of Medial Axes",
"doi": null,
"abstractUrl": "/proceedings-article/isvd/2006/26300040/12OmNxA3YVu",
"parentPublication": {
"id": "proceedings/isvd/2006/2630/0",
"title": "2006 3rd International Symposium on Voronoi Diagrams in Science and Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aici/2009/3816/2/3816b544",
"title": "Medial Axis Extraction Using Growing Neural Gas",
"doi": null,
"abstractUrl": "/proceedings-article/aici/2009/3816b544/12OmNyS6REf",
"parentPublication": {
"id": "proceedings/aici/2009/3816/2",
"title": "2009 International Conference on Artificial Intelligence and Computational Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2009/05/ttp2009050900",
"title": "Transitions of the 3D Medial Axis under a One-Parameter Family of Deformations",
"doi": null,
"abstractUrl": "/journal/tp/2009/05/ttp2009050900/13rRUwghda9",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/1996/01/v0062",
"title": "Shape Description By Medial Surface Construction",
"doi": null,
"abstractUrl": "/journal/tg/1996/01/v0062/13rRUx0gepQ",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2004/02/i0238",
"title": "A Formal Classification of 3D Medial Axis Points and Their Local Geometry",
"doi": null,
"abstractUrl": "/journal/tp/2004/02/i0238/13rRUxAAT28",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzYeB3H",
"title": "11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,",
"acronym": "icpr",
"groupId": "1000545",
"volume": "0",
"displayVolume": "0",
"year": "1992",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNqBKTLd",
"doi": "10.1109/ICPR.1992.202026",
"title": "Interval skeletons",
"normalizedTitle": "Interval skeletons",
"abstract": "A skeleton method based on interval coding of a binary image is shown to preserve the homotopy of the image. With a computational complexity linear in the number of intervals, it is typically an order of magnitude faster than other fast skeleton methods. Thinness, reconstructability, and other shape-representation properties of the method are discussed.<>",
"abstracts": [
{
"abstractType": "Regular",
"content": "A skeleton method based on interval coding of a binary image is shown to preserve the homotopy of the image. With a computational complexity linear in the number of intervals, it is typically an order of magnitude faster than other fast skeleton methods. Thinness, reconstructability, and other shape-representation properties of the method are discussed.<>",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "A skeleton method based on interval coding of a binary image is shown to preserve the homotopy of the image. With a computational complexity linear in the number of intervals, it is typically an order of magnitude faster than other fast skeleton methods. Thinness, reconstructability, and other shape-representation properties of the method are discussed.",
"fno": "00202026",
"keywords": [
"Computational Complexity",
"Image Coding",
"Image Reconstruction",
"Image Sequences",
"Binary Image Interval Coding",
"Image Homotropy",
"Thinness",
"Skeleton Method",
"Computational Complexity",
"Reconstructability",
"Shape Representation Properties",
"Skeleton",
"Image Coding",
"Image Reconstruction",
"Shape",
"Euclidean Distance",
"Humans",
"Genetics",
"Computational Complexity",
"Topology",
"Morphology"
],
"authors": [
{
"affiliation": "MRC Human Genetics Unit, Edinburgh, UK",
"fullName": "J. Piper",
"givenName": "J.",
"surname": "Piper",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "1992-01-01T00:00:00",
"pubType": "proceedings",
"pages": "468-471",
"year": "1992",
"issn": null,
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "00202025",
"articleId": "12OmNBl6EHc",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "00202027",
"articleId": "12OmNx19jTU",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/1990/2062/1/00118234",
"title": "Generating skeletons and centerlines from the medial axis transform",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1990/00118234/12OmNAY79mR",
"parentPublication": {
"id": "proceedings/icpr/1990/2062/1",
"title": "Proceedings 10th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1992/2855/0/00223248",
"title": "Generating connected skeletons for exact and approximate reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1992/00223248/12OmNrFTr8T",
"parentPublication": {
"id": "proceedings/cvpr/1992/2855/0",
"title": "Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1994/6265/1/00576381",
"title": "Using polyballs to approximate shapes and skeletons",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1994/00576381/12OmNwDACpP",
"parentPublication": {
"id": "proceedings/icpr/1994/6265/1",
"title": "Proceedings of 12th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdip/2009/3565/0/3565a260",
"title": "A Survey on Skeletons in Digital Image Processing",
"doi": null,
"abstractUrl": "/proceedings-article/icdip/2009/3565a260/12OmNxEBzj0",
"parentPublication": {
"id": "proceedings/icdip/2009/3565/0",
"title": "Digital Image Processing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1992/2855/0/00223226",
"title": "Voronoi skeletons: theory and applications",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1992/00223226/12OmNxH9Xdu",
"parentPublication": {
"id": "proceedings/cvpr/1992/2855/0",
"title": "Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ssiai/2006/0069/0/01633715",
"title": "On the Properties of Morphological Skeletons of Discrete Binary Image Using Double Structuring Elements",
"doi": null,
"abstractUrl": "/proceedings-article/ssiai/2006/01633715/12OmNy2rRWH",
"parentPublication": {
"id": "proceedings/ssiai/2006/0069/0",
"title": "7th IEEE Southwest Symposium on Image Analysis and Interpretation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1992/2920/0/00202034",
"title": "Multi-resolution skeletons without explicit image smoothing",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1992/00202034/12OmNy5hRlV",
"parentPublication": {
"id": "proceedings/icpr/1992/2920/0",
"title": "11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbms/1992/2742/0/00244946",
"title": "Morphological skeletonization for medical image compression",
"doi": null,
"abstractUrl": "/proceedings-article/cbms/1992/00244946/12OmNzAohYL",
"parentPublication": {
"id": "proceedings/cbms/1992/2742/0",
"title": "Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2001/11/i1296",
"title": "Hierarchical Decomposition of Multiscale Skeletons",
"doi": null,
"abstractUrl": "/journal/tp/2001/11/i1296/13rRUILc8g5",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/1992/06/i0653",
"title": "Fast Homotopy-Preserving Skeletons Using Mathematical Morphology",
"doi": null,
"abstractUrl": "/journal/tp/1992/06/i0653/13rRUxZ0o2p",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNqBtiPB",
"title": "2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)",
"acronym": "3pgcic",
"groupId": "1800224",
"volume": "0",
"displayVolume": "0",
"year": "2015",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNBDQbex",
"doi": "10.1109/3PGCIC.2015.126",
"title": "Research on Web Intelligent Robot Based on Virtual Reality",
"normalizedTitle": "Research on Web Intelligent Robot Based on Virtual Reality",
"abstract": "With the development of Internet +, information retrieval, information analysis and intelligent decision support system are also undergoing a rapid development. Intelligent interaction is a key problem for the popularization and application of these systems. Web intelligent robots can solve this problem well. In this paper, Web intelligent robot technology based on Virtual Reality is further studied and a web intelligent robot is designed and developed by using Unity3D technology. In the virtual scene, the robot can interact with the third person perspective. This interactive feature allows the user to experience the perfect state of immersion and interaction. At the same time, the users' need for intelligent interaction in the network world has been a perfect embodiment.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With the development of Internet +, information retrieval, information analysis and intelligent decision support system are also undergoing a rapid development. Intelligent interaction is a key problem for the popularization and application of these systems. Web intelligent robots can solve this problem well. In this paper, Web intelligent robot technology based on Virtual Reality is further studied and a web intelligent robot is designed and developed by using Unity3D technology. In the virtual scene, the robot can interact with the third person perspective. This interactive feature allows the user to experience the perfect state of immersion and interaction. At the same time, the users' need for intelligent interaction in the network world has been a perfect embodiment.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "With the development of Internet +, information retrieval, information analysis and intelligent decision support system are also undergoing a rapid development. Intelligent interaction is a key problem for the popularization and application of these systems. Web intelligent robots can solve this problem well. In this paper, Web intelligent robot technology based on Virtual Reality is further studied and a web intelligent robot is designed and developed by using Unity3D technology. In the virtual scene, the robot can interact with the third person perspective. This interactive feature allows the user to experience the perfect state of immersion and interaction. At the same time, the users' need for intelligent interaction in the network world has been a perfect embodiment.",
"fno": "9473a795",
"keywords": [
"Intelligent Robots",
"Robot Kinematics",
"Legged Locomotion",
"Virtual Reality",
"Solid Modeling",
"Immersion",
"Web",
"Intelligent Robot",
"Virtual Reality",
"Intelligent Interaction"
],
"authors": [
{
"affiliation": null,
"fullName": "Shuxia Ren",
"givenName": "Shuxia",
"surname": "Ren",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Dakun Zhang",
"givenName": "Dakun",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Kunliang Liu",
"givenName": "Kunliang",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Tianyu Lu",
"givenName": "Tianyu",
"surname": "Lu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Guozhi Song",
"givenName": "Guozhi",
"surname": "Song",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "3pgcic",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2015-11-01T00:00:00",
"pubType": "proceedings",
"pages": "795-799",
"year": "2015",
"issn": null,
"isbn": "978-1-4673-9473-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "9473a791",
"articleId": "12OmNx9nGG6",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "9473a800",
"articleId": "12OmNznkKbW",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iciev/2016/1269/0/07760024",
"title": "Gesture based wireless shadow robot",
"doi": null,
"abstractUrl": "/proceedings-article/iciev/2016/07760024/12OmNAKuoSn",
"parentPublication": {
"id": "proceedings/iciev/2016/1269/0",
"title": "2016 International Conference on Informatics, Electronics and Vision (ICIEV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccnea/2017/3981/0/3981a426",
"title": "Motion Simulation of Bionic Hexapod Robot Based on Virtual Prototyping Technology",
"doi": null,
"abstractUrl": "/proceedings-article/iccnea/2017/3981a426/12OmNC36tQV",
"parentPublication": {
"id": "proceedings/iccnea/2017/3981/0",
"title": "2017 International Conference on Computer Network, Electronic and Automation (ICCNEA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccairo/2017/6536/0/6536a015",
"title": "Modeling of Biped Robot Archie",
"doi": null,
"abstractUrl": "/proceedings-article/iccairo/2017/6536a015/12OmNCdBDHd",
"parentPublication": {
"id": "proceedings/iccairo/2017/6536/0",
"title": "2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icoac/2012/5583/0/06416851",
"title": "Tarantula bot: Rescue assist tele robot",
"doi": null,
"abstractUrl": "/proceedings-article/icoac/2012/06416851/12OmNCeaPVJ",
"parentPublication": {
"id": "proceedings/icoac/2012/5583/0",
"title": "2012 Fourth International Conference on Advanced Computing (ICoAC 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cscs/2017/1839/0/07968605",
"title": "Modelling Inverse Kinematics for Virtual Environment Robot Simulation",
"doi": null,
"abstractUrl": "/proceedings-article/cscs/2017/07968605/12OmNwkhTic",
"parentPublication": {
"id": "proceedings/cscs/2017/1839/0",
"title": "2017 21st International Conference on Control Systems and Computer Science (CSCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2014/6636/0/6636a088",
"title": "Analysis and Design of Humanoid Robot Dance",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2014/6636a088/12OmNwpoFMM",
"parentPublication": {
"id": "proceedings/icicta/2014/6636/0",
"title": "2014 7th International Conference on Intelligent Computation Technology and Automation (ICICTA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2016/2535/0/2535a799",
"title": "A Stable Walking Strategy of Quadruped Robot Based on Foot Trajectory Planning",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2016/2535a799/12OmNzgwmMR",
"parentPublication": {
"id": "proceedings/icisce/2016/2535/0",
"title": "2016 3rd International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2018/3365/0/08446521",
"title": "Extended Abstract: Natural Human-Robot Interaction in Virtual Reality Telepresence Systems",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446521/13bd1ftOBCZ",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2019/6092/0/609200a379",
"title": "Hardware Architecture Design of Intelligent Fighting Robot",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2019/609200a379/1i5m38Lna3C",
"parentPublication": {
"id": "proceedings/cis/2019/6092/0",
"title": "2019 15th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/crc/2019/4620/0/462000a070",
"title": "The Analysis of Key Technologies for Advanced Intelligent Quadruped Robots",
"doi": null,
"abstractUrl": "/proceedings-article/crc/2019/462000a070/1iTuIekx6CY",
"parentPublication": {
"id": "proceedings/crc/2019/4620/0",
"title": "2019 4th International Conference on Control, Robotics and Cybernetics (CRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNBZYToD",
"title": "2015 Third International Conference on Image Information Processing (ICIIP)",
"acronym": "iciip",
"groupId": "1800591",
"volume": "0",
"displayVolume": "0",
"year": "2015",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNrJ11HQ",
"doi": "10.1109/ICIIP.2015.7414826",
"title": "A real-time ball trajectory follower using Robot Operating System",
"normalizedTitle": "A real-time ball trajectory follower using Robot Operating System",
"abstract": "Modern sports events involve multiple cameras recording the event with very high quality of video. The multiple camera system is complex and generally requires a team of trained men to get the best shot of the ball especially in challenging cases such as line calls in tennis events, corner detection in football events etc. This paper suggests a simple, economical and less time consuming solution using an object tracking algorithm implemented via an Unmanned Aerial Vehicle (UAV). The UAV used in the paper is the Vertical Take-off and Landing (VTOL) Parrot A.R. Drone 2.0 which acts as a moving aerial platform and aims at tracking fast moving ground objects by processing real-time camera feed. Presently, the images from the front camera of the drone are obtained and the moving ground object detected by colour segmentation techniques. The detected object is continuously tracked by centering the image frame. Open Source Computer Vision libraries (OpenCV) are used to process the images obtained from the drone which is controlled by an environment created using Robot Operating System (ROS).",
"abstracts": [
{
"abstractType": "Regular",
"content": "Modern sports events involve multiple cameras recording the event with very high quality of video. The multiple camera system is complex and generally requires a team of trained men to get the best shot of the ball especially in challenging cases such as line calls in tennis events, corner detection in football events etc. This paper suggests a simple, economical and less time consuming solution using an object tracking algorithm implemented via an Unmanned Aerial Vehicle (UAV). The UAV used in the paper is the Vertical Take-off and Landing (VTOL) Parrot A.R. Drone 2.0 which acts as a moving aerial platform and aims at tracking fast moving ground objects by processing real-time camera feed. Presently, the images from the front camera of the drone are obtained and the moving ground object detected by colour segmentation techniques. The detected object is continuously tracked by centering the image frame. Open Source Computer Vision libraries (OpenCV) are used to process the images obtained from the drone which is controlled by an environment created using Robot Operating System (ROS).",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Modern sports events involve multiple cameras recording the event with very high quality of video. The multiple camera system is complex and generally requires a team of trained men to get the best shot of the ball especially in challenging cases such as line calls in tennis events, corner detection in football events etc. This paper suggests a simple, economical and less time consuming solution using an object tracking algorithm implemented via an Unmanned Aerial Vehicle (UAV). The UAV used in the paper is the Vertical Take-off and Landing (VTOL) Parrot A.R. Drone 2.0 which acts as a moving aerial platform and aims at tracking fast moving ground objects by processing real-time camera feed. Presently, the images from the front camera of the drone are obtained and the moving ground object detected by colour segmentation techniques. The detected object is continuously tracked by centering the image frame. Open Source Computer Vision libraries (OpenCV) are used to process the images obtained from the drone which is controlled by an environment created using Robot Operating System (ROS).",
"fno": "07414826",
"keywords": [
"Robots",
"Libraries",
"Mathematical Model",
"Instruments",
"Process Control",
"PID Controller",
"Ball Follower",
"Unmanned Aerial Vehicle",
"Robot Operating System"
],
"authors": [
{
"affiliation": "National Institute of Technology, Tiruchirappalli, India",
"fullName": "Himanshi Yadav",
"givenName": "Himanshi",
"surname": "Yadav",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Department of Electrical Engineering, Indian Institute of Technology, Delhi, India",
"fullName": "Siddharth Srivastava",
"givenName": "Siddharth",
"surname": "Srivastava",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Department of Electrical Engineering, Indian Institute of Technology, Delhi, India",
"fullName": "Prerana Mukherjee",
"givenName": "Prerana",
"surname": "Mukherjee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Department of Electrical Engineering, Indian Institute of Technology, Delhi, India",
"fullName": "Brejesh Lall",
"givenName": "Brejesh",
"surname": "Lall",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iciip",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2015-12-01T00:00:00",
"pubType": "proceedings",
"pages": "511-515",
"year": "2015",
"issn": null,
"isbn": "978-1-5090-0148-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "07414825",
"articleId": "12OmNApcu7B",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07414827",
"articleId": "12OmNvSbBOX",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2008/2174/0/04761422",
"title": "Anomalous trajectory patterns detection",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2008/04761422/12OmNAq3hRm",
"parentPublication": {
"id": "proceedings/icpr/2008/2174/0",
"title": "ICPR 2008 19th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1992/2720/0/00220052",
"title": "Position estimation for a mobile robot using vision and odometry",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1992/00220052/12OmNBdJ5hz",
"parentPublication": {
"id": "proceedings/robot/1992/2720/0",
"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cts/2016/2300/0/07871011",
"title": "Development of an Autonomous Ball-Picking Robot",
"doi": null,
"abstractUrl": "/proceedings-article/cts/2016/07871011/12OmNvDqsId",
"parentPublication": {
"id": "proceedings/cts/2016/2300/0",
"title": "2016 International Conference on Collaboration Technologies and Systems (CTS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sbr-lars/2012/4906/0/4906a168",
"title": "High-Level Nonlinear Underactuated Controller for a Leader-Follower Formation Involving a Miniature Helicopter and a Ground Robot",
"doi": null,
"abstractUrl": "/proceedings-article/sbr-lars/2012/4906a168/12OmNwF0BKY",
"parentPublication": {
"id": "proceedings/sbr-lars/2012/4906/0",
"title": "Brazilian Robotics Symposium and Latin American Robotics Symposium (SBR-LARS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/avss/2017/2939/0/08078558",
"title": "Deep cross-domain flying object classification for robust UAV detection",
"doi": null,
"abstractUrl": "/proceedings-article/avss/2017/08078558/12OmNwFicYj",
"parentPublication": {
"id": "proceedings/avss/2017/2939/0",
"title": "2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2012/2216/0/06460606",
"title": "Efficient UAV video event summarization",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460606/12OmNwvVrzO",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vlsid/2022/8505/0/850500a180",
"title": "Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing",
"doi": null,
"abstractUrl": "/proceedings-article/vlsid/2022/850500a180/1GFaBaZ3BMk",
"parentPublication": {
"id": "proceedings/vlsid/2022/8505/0",
"title": "2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmeae/2021/9540/0/954000a146",
"title": "Trajectory tracking based on a saturated PD controller on SE(3) for a micro coaxial drone",
"doi": null,
"abstractUrl": "/proceedings-article/icmeae/2021/954000a146/1GZjC7hXZMk",
"parentPublication": {
"id": "proceedings/icmeae/2021/9540/0",
"title": "2021 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmeae/2018/9190/0/919100a038",
"title": "Real-Time Drone (UAV) Trajectory Generation and Tracking by Optical Flow",
"doi": null,
"abstractUrl": "/proceedings-article/icmeae/2018/919100a038/1b65V0DnRny",
"parentPublication": {
"id": "proceedings/icmeae/2018/9190/0",
"title": "2018 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2021/0191/0/019100d892",
"title": "JanusNet: Detection of Moving Objects from UAV Platforms",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2021/019100d892/1yNhKNB2avS",
"parentPublication": {
"id": "proceedings/iccvw/2021/0191/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNvAiSpZ",
"title": "2015 IEEE Virtual Reality (VR)",
"acronym": "vr",
"groupId": "1000791",
"volume": "0",
"displayVolume": "0",
"year": "2015",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxIzWOO",
"doi": "10.1109/VR.2015.7223421",
"title": "Flying robot manipulation system using a virtual plane",
"normalizedTitle": "Flying robot manipulation system using a virtual plane",
"abstract": "The flexible movements of flying robots make it difficult for novices to manipulate them precisely with controllers such as a joystick and a proportional radio system. Moreover, the mapping of instructions between a robot and its reactions is not necessarily intuitive for users. We propose manipulation methods for flying robots using augmented reality technologies. In the proposed system, a virtual plane is superimposed on a flying robot and users control the robot by manipulating the virtual plane and drawing a moving path on it. We present the design and implementation of our system and describe experiments conducted to evaluate our methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The flexible movements of flying robots make it difficult for novices to manipulate them precisely with controllers such as a joystick and a proportional radio system. Moreover, the mapping of instructions between a robot and its reactions is not necessarily intuitive for users. We propose manipulation methods for flying robots using augmented reality technologies. In the proposed system, a virtual plane is superimposed on a flying robot and users control the robot by manipulating the virtual plane and drawing a moving path on it. We present the design and implementation of our system and describe experiments conducted to evaluate our methods.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The flexible movements of flying robots make it difficult for novices to manipulate them precisely with controllers such as a joystick and a proportional radio system. Moreover, the mapping of instructions between a robot and its reactions is not necessarily intuitive for users. We propose manipulation methods for flying robots using augmented reality technologies. In the proposed system, a virtual plane is superimposed on a flying robot and users control the robot by manipulating the virtual plane and drawing a moving path on it. We present the design and implementation of our system and describe experiments conducted to evaluate our methods.",
"fno": "07223421",
"keywords": [
"Prototypes",
"Service Robots",
"Augmented Reality",
"Training",
"Robot Control",
"Unmanned Aerial Vehicles",
"I 2 9 Artificial Intelligence Robotics Operator Interfaces",
"H 5 1 Information Interfaces And Presentation Multimedia Information Systems Artificial Augmented And Virtual Realities"
],
"authors": [
{
"affiliation": "The University of Tokyo",
"fullName": "Kazuya Yonezawa",
"givenName": "Kazuya",
"surname": "Yonezawa",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Information Technology Center, The University of Tokyo",
"fullName": "Takefumi Ogawa",
"givenName": "Takefumi",
"surname": "Ogawa",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2015-03-01T00:00:00",
"pubType": "proceedings",
"pages": "313-314",
"year": "2015",
"issn": null,
"isbn": "978-1-4799-1727-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "07223420",
"articleId": "12OmNqEAT6S",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07223422",
"articleId": "12OmNzSyCgR",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/aqtr/2010/6724/1/05520876",
"title": "Autonomous mobile robot with displacements in a vertical plane and applications in cleaning services",
"doi": null,
"abstractUrl": "/proceedings-article/aqtr/2010/05520876/12OmNAIMO5p",
"parentPublication": {
"id": "proceedings/aqtr/2010/6724/1",
"title": "International Conference on Automation, Quality and Testing, Robotics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isdea/2010/8333/2/05743377",
"title": "Path Planning of Flying Robot for Powerline Inspection Based on Improved Particle Swarm Optimization",
"doi": null,
"abstractUrl": "/proceedings-article/isdea/2010/05743377/12OmNAtaRYP",
"parentPublication": {
"id": "proceedings/isdea/2010/8333/2",
"title": "2010 International Conference on Intelligent System Design and Engineering Application",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hicss/2003/1874/5/187450125b",
"title": "World Embedded Interfaces for Human-Robot Interaction",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2003/187450125b/12OmNBC8AxP",
"parentPublication": {
"id": "proceedings/hicss/2003/1874/5",
"title": "36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1991/2163/0/00132004",
"title": "Dual arm coordination in space free-flying robot",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1991/00132004/12OmNC4eSkv",
"parentPublication": {
"id": "proceedings/robot/1991/2163/0",
"title": "Proceedings. 1991 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/intetain/2015/0061/0/07325482",
"title": "Robot devastation: Using DIY low-cost platforms for multiplayer interaction in an augmented reality game",
"doi": null,
"abstractUrl": "/proceedings-article/intetain/2015/07325482/12OmNyQYt2r",
"parentPublication": {
"id": "proceedings/intetain/2015/0061/0",
"title": "2015 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icita/2005/2316/2/231620015",
"title": "Virtual Repulsive Force in Competitive Multi-Robot Teleoperation",
"doi": null,
"abstractUrl": "/proceedings-article/icita/2005/231620015/12OmNyeECBf",
"parentPublication": {
"id": "proceedings/icita/2005/2316/2",
"title": "Information Technology and Applications, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icece/2010/4031/0/4031b470",
"title": "Path Planning of Flying Robot for Overhead Powerline Inspection Based on Key Points Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/icece/2010/4031b470/12OmNz2kqeY",
"parentPublication": {
"id": "proceedings/icece/2010/4031/0",
"title": "Electrical and Control Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2009/3791/0/3791a013",
"title": "Robot Programming Using Augmented Reality",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2009/3791a013/12OmNz5JBSP",
"parentPublication": {
"id": "proceedings/cw/2009/3791/0",
"title": "2009 International Conference on CyberWorlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2018/08/07983006",
"title": "FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras",
"doi": null,
"abstractUrl": "/journal/tg/2018/08/07983006/13rRUxYrbUO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2019/4765/0/476500a165",
"title": "InvisibleRobot: Facilitating Robot Manipulation Through Diminished Reality",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2019/476500a165/1gyskfJT9f2",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2019/4765/0",
"title": "2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNykCcdi",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"acronym": "cvprw",
"groupId": "1001809",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNxiKs6j",
"doi": "10.1109/CVPRW.2016.111",
"title": "Vision Based Autonomous Orientational Control for Aerial Manipulation via On-board FPGA",
"normalizedTitle": "Vision Based Autonomous Orientational Control for Aerial Manipulation via On-board FPGA",
"abstract": "We describe an FPGA-based on-board control system for autonomous orientation of an aerial robot to assist aerial manipulation tasks. The system is able to apply yaw control to aid an operator to precisely position a drone when it is nearby a bar-like object. This is achieved by applying parallel Hough transform enhanced with a novel image space separation method, enabling highly reliable results in various circumstances combined with high performance. The feasibility of this approach is shown by applying the system to a multi-rotor aerial robot equipped with an upward directed robotic hand on top of the airframe developed for high altitude manipulation tasks. In order to grasp a barlike object, orientation of the bar object is observed from the image data obtained by a monocular camera mounted on the robot. This data is then analyzed by the on-board FPGA system to control yaw angle of the aerial robot. In experiments, reliable yaw-orientation control of the aerial robot is achieved.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We describe an FPGA-based on-board control system for autonomous orientation of an aerial robot to assist aerial manipulation tasks. The system is able to apply yaw control to aid an operator to precisely position a drone when it is nearby a bar-like object. This is achieved by applying parallel Hough transform enhanced with a novel image space separation method, enabling highly reliable results in various circumstances combined with high performance. The feasibility of this approach is shown by applying the system to a multi-rotor aerial robot equipped with an upward directed robotic hand on top of the airframe developed for high altitude manipulation tasks. In order to grasp a barlike object, orientation of the bar object is observed from the image data obtained by a monocular camera mounted on the robot. This data is then analyzed by the on-board FPGA system to control yaw angle of the aerial robot. In experiments, reliable yaw-orientation control of the aerial robot is achieved.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We describe an FPGA-based on-board control system for autonomous orientation of an aerial robot to assist aerial manipulation tasks. The system is able to apply yaw control to aid an operator to precisely position a drone when it is nearby a bar-like object. This is achieved by applying parallel Hough transform enhanced with a novel image space separation method, enabling highly reliable results in various circumstances combined with high performance. The feasibility of this approach is shown by applying the system to a multi-rotor aerial robot equipped with an upward directed robotic hand on top of the airframe developed for high altitude manipulation tasks. In order to grasp a barlike object, orientation of the bar object is observed from the image data obtained by a monocular camera mounted on the robot. This data is then analyzed by the on-board FPGA system to control yaw angle of the aerial robot. In experiments, reliable yaw-orientation control of the aerial robot is achieved.",
"fno": "1437a854",
"keywords": [
"Attitude Control",
"Autonomous Aerial Vehicles",
"Cameras",
"Field Programmable Gate Arrays",
"Helicopters",
"Hough Transforms",
"Position Control",
"Robot Vision",
"Vision Based Autonomous Orientational Control",
"Aerial Manipulation",
"FPGA Based Onboard Control System",
"Autonomous Aerial Robot Orientation",
"Yaw Control",
"Drone Position Control",
"Bar Like Object",
"Parallel Hough Transform",
"Image Space Separation Method",
"Multirotor Aerial Robot",
"Upward Directed Robotic Hand",
"High Altitude Manipulation Tasks",
"Monocular Camera",
"Onboard FPGA System",
"Yaw Orientation Control",
"Robots",
"Field Programmable Gate Arrays",
"Unmanned Aerial Vehicles",
"Transforms",
"Bars",
"Cameras",
"Random Access Memory"
],
"authors": [
{
"affiliation": null,
"fullName": "Suphachart Leewiwatwong",
"givenName": "Suphachart",
"surname": "Leewiwatwong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Syohei Shimahara",
"givenName": "Syohei",
"surname": "Shimahara",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Robert Ladig",
"givenName": "Robert",
"surname": "Ladig",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Kazuhiro Shimonomura",
"givenName": "Kazuhiro",
"surname": "Shimonomura",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvprw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-06-01T00:00:00",
"pubType": "proceedings",
"pages": "854-860",
"year": "2016",
"issn": "2160-7516",
"isbn": "978-1-5090-1437-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "1437a845",
"articleId": "12OmNrJiCRz",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "1437a861",
"articleId": "12OmNBqMDjb",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/med/2006/1/0/04124965",
"title": "Feature Matching Algorithms for Machine Vision Based Autonomous Aerial Refueling",
"doi": null,
"abstractUrl": "/proceedings-article/med/2006/04124965/12OmNBCqbDo",
"parentPublication": {
"id": "proceedings/med/2006/1/0",
"title": "Proceedings of the 14th Mediterranean Conference on Control and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/smartcomp/2017/6517/0/07947014",
"title": "I Am the UAV: A Wearable Approach for Manipulation of Unmanned Aerial Vehicle",
"doi": null,
"abstractUrl": "/proceedings-article/smartcomp/2017/07947014/12OmNqBbHuU",
"parentPublication": {
"id": "proceedings/smartcomp/2017/6517/0",
"title": "2017 IEEE International Conference on Smart Computing (SMARTCOMP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichit/2006/2674/2/04021223",
"title": "Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle",
"doi": null,
"abstractUrl": "/proceedings-article/ichit/2006/04021223/12OmNqH9hiA",
"parentPublication": {
"id": "proceedings/ichit/2006/2674/2",
"title": "2006 International Conference on Hybrid Information Technology",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/1999/0481/0/04810161",
"title": "Pose Estimation of Autonomous Dirigibles Using Artificial Landmarks",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/1999/04810161/12OmNvBrgF9",
"parentPublication": {
"id": "proceedings/sibgrapi/1999/0481/0",
"title": "XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2015/1727/0/07223421",
"title": "Flying robot manipulation system using a virtual plane",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2015/07223421/12OmNxIzWOO",
"parentPublication": {
"id": "proceedings/vr/2015/1727/0",
"title": "2015 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/med/2006/1/0/04124967",
"title": "Vision-Based Autonomous Probe and Drogue Aerial Refueling",
"doi": null,
"abstractUrl": "/proceedings-article/med/2006/04124967/12OmNy5R3AH",
"parentPublication": {
"id": "proceedings/med/2006/1/0",
"title": "Proceedings of the 14th Mediterranean Conference on Control and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icarsc/2016/2255/0/07781969",
"title": "ISEP/INESC TEC Aerial Robotics Team for Search and Rescue Operations at the EuRathlon Challenge 2015",
"doi": null,
"abstractUrl": "/proceedings-article/icarsc/2016/07781969/12OmNyugyUp",
"parentPublication": {
"id": "proceedings/icarsc/2016/2255/0",
"title": "2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/apwc-on-cse/2016/5753/0/07941936",
"title": "Autonomous Elevator Inspection with Unmanned Aerial Vehicle",
"doi": null,
"abstractUrl": "/proceedings-article/apwc-on-cse/2016/07941936/17D45WaTkjt",
"parentPublication": {
"id": "proceedings/apwc-on-cse/2016/5753/0",
"title": "2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acsos-c/2020/8414/0/09196388",
"title": "SoftwarePilot: Fully Autonomous Aerial Systems Made Easier",
"doi": null,
"abstractUrl": "/proceedings-article/acsos-c/2020/09196388/1n90NOQ6KKk",
"parentPublication": {
"id": "proceedings/acsos-c/2020/8414/0",
"title": "2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aiam/2020/9986/0/998600a057",
"title": "Indoor Vision Navigation and Target Tracking System for Aerial Robot",
"doi": null,
"abstractUrl": "/proceedings-article/aiam/2020/998600a057/1tweTgkjr8Y",
"parentPublication": {
"id": "proceedings/aiam/2020/9986/0",
"title": "2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1AtQ1yO7FTy",
"title": "2022 International Conference on Information Networking (ICOIN)",
"acronym": "icoin",
"groupId": "1000363",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1AtQ3LKJ8Dm",
"doi": "10.1109/ICOIN53446.2022.9687268",
"title": "Adaptive Drone Identification and Neutralization Scheme for Real-Time Military Tactical Operations",
"normalizedTitle": "Adaptive Drone Identification and Neutralization Scheme for Real-Time Military Tactical Operations",
"abstract": "The surging proliferation in the deployment of unmanned aerial vehicles (UAVs) in various domains has resulted into unsolicited intrusion into private properties and protected areas thereby posing threat to national security. This paper proposed an adaptive scenario-based approach for detecting drone invasion using enhanced YOLOv5 deep learning model to detect different drones and identify attached objects operating under any environment, size, speed, or shape. The dataset consists of 6 drone models and 8 attached weapons manually generated and preprocessed to form samples. In terms of accuracy, sensitivity, and timeliness, the result shows that our model achieved superior detection precision of 100%, sensitivity of 99.9%, F1-score of 87.2% for weapons identification at a shorter time of 0. 021s than other models. The high detection accuracy undoubtedly makes our model well suited for real-time drone monitoring and countering of illegal drones in military offensives with minimal resource usage.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The surging proliferation in the deployment of unmanned aerial vehicles (UAVs) in various domains has resulted into unsolicited intrusion into private properties and protected areas thereby posing threat to national security. This paper proposed an adaptive scenario-based approach for detecting drone invasion using enhanced YOLOv5 deep learning model to detect different drones and identify attached objects operating under any environment, size, speed, or shape. The dataset consists of 6 drone models and 8 attached weapons manually generated and preprocessed to form samples. In terms of accuracy, sensitivity, and timeliness, the result shows that our model achieved superior detection precision of 100%, sensitivity of 99.9%, F1-score of 87.2% for weapons identification at a shorter time of 0. 021s than other models. The high detection accuracy undoubtedly makes our model well suited for real-time drone monitoring and countering of illegal drones in military offensives with minimal resource usage.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The surging proliferation in the deployment of unmanned aerial vehicles (UAVs) in various domains has resulted into unsolicited intrusion into private properties and protected areas thereby posing threat to national security. This paper proposed an adaptive scenario-based approach for detecting drone invasion using enhanced YOLOv5 deep learning model to detect different drones and identify attached objects operating under any environment, size, speed, or shape. The dataset consists of 6 drone models and 8 attached weapons manually generated and preprocessed to form samples. In terms of accuracy, sensitivity, and timeliness, the result shows that our model achieved superior detection precision of 100%, sensitivity of 99.9%, F1-score of 87.2% for weapons identification at a shorter time of 0. 021s than other models. The high detection accuracy undoubtedly makes our model well suited for real-time drone monitoring and countering of illegal drones in military offensives with minimal resource usage.",
"fno": "09687268",
"keywords": [
"Autonomous Aerial Vehicles",
"Control Engineering Computing",
"Deep Learning Artificial Intelligence",
"Military Computing",
"Military Robotics",
"National Security",
"Object Detection",
"Robot Vision",
"Weapons",
"Adaptive Drone Identification",
"Neutralization Scheme",
"Real Time Military Tactical Operations",
"Unmanned Aerial Vehicles",
"UAV",
"Unsolicited Intrusion",
"Private Properties",
"Protected Areas",
"National Security",
"Adaptive Scenario Based Approach",
"Drone Invasion Detection",
"Enhanced YOL Ov 5 Deep Learning Model",
"Weapons Identification",
"Real Time Drone Monitoring",
"Illegal Drones",
"Military Offensives",
"Deep Learning",
"Adaptation Models",
"Sensitivity",
"Adaptive Systems",
"Computational Modeling",
"Weapons",
"Autonomous Aerial Vehicles",
"Deep Learning",
"Drone",
"Military",
"Surveillance",
"Detection"
],
"authors": [
{
"affiliation": "Kumoh National Institute of Technology,Department of IT Convergence Engineering,Gumi,South Korea",
"fullName": "Simeon Okechukwu Ajakwe",
"givenName": "Simeon Okechukwu",
"surname": "Ajakwe",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Kumoh National Institute of Technology,Department of IT Convergence Engineering,Gumi,South Korea",
"fullName": "Vivian Ukamaka Ihekoronye",
"givenName": "Vivian Ukamaka",
"surname": "Ihekoronye",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Kumoh National Institute of Technology,Department of IT Convergence Engineering,Gumi,South Korea",
"fullName": "Rubina Akter",
"givenName": "Rubina",
"surname": "Akter",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Kumoh National Institute of Technology,Department of IT Convergence Engineering,Gumi,South Korea",
"fullName": "Dong-Seong Kim",
"givenName": "Dong-Seong",
"surname": "Kim",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Kumoh National Institute of Technology,Department of IT Convergence Engineering,Gumi,South Korea",
"fullName": "Jae Min Lee",
"givenName": "Jae Min",
"surname": "Lee",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icoin",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-01-01T00:00:00",
"pubType": "proceedings",
"pages": "380-384",
"year": "2022",
"issn": "1976-7684",
"isbn": "978-1-6654-1332-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09687150",
"articleId": "1AtQdbsfK8M",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09687136",
"articleId": "1AtQ3sHkDgQ",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/waina/2016/2461/0/2461a248",
"title": "SAN: Self-Adaptive Navigation for Drone Battery Charging in Wireless Drone Networks",
"doi": null,
"abstractUrl": "/proceedings-article/waina/2016/2461a248/12OmNAnMuEj",
"parentPublication": {
"id": "proceedings/waina/2016/2461/0",
"title": "2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/prdc/2021/2476/0/247600a143",
"title": "Are you for Real? Authentication in Dynamic IoT Systems",
"doi": null,
"abstractUrl": "/proceedings-article/prdc/2021/247600a143/1A6BOaZbnuE",
"parentPublication": {
"id": "proceedings/prdc/2021/2476/0",
"title": "2021 IEEE 26th Pacific Rim International Symposium on Dependable Computing (PRDC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ccict/2022/7224/0/722400a467",
"title": "Detection of anomalous unmanned aerial vehicles: A systematic study, challenges, and future trends",
"doi": null,
"abstractUrl": "/proceedings-article/ccict/2022/722400a467/1HpDRBDKl6o",
"parentPublication": {
"id": "proceedings/ccict/2022/7224/0",
"title": "2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cscc/2022/8186/0/818600a085",
"title": "Airport defense systems against drones attacks",
"doi": null,
"abstractUrl": "/proceedings-article/cscc/2022/818600a085/1KaFryKh4re",
"parentPublication": {
"id": "proceedings/cscc/2022/8186/0",
"title": "2022 26th International Conference on Circuits, Systems, Communications and Computers (CSCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2022/9744/0/974400a812",
"title": "Automated Drone Classification for Internet of Drones Based on a Hybrid Transformer Model",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2022/974400a812/1MrFWRLtwxq",
"parentPublication": {
"id": "proceedings/ictai/2022/9744/0",
"title": "2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2019/4765/0/476500a167",
"title": "DroneCamo: Modifying Human-Drone Comfort via Augmented Reality",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2019/476500a167/1gysj7RryWk",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2019/4765/0",
"title": "2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/srds/2021/3819/0/381900a331",
"title": "DragonFly: Drone-Assisted High-Rise Monitoring for Fire Safety",
"doi": null,
"abstractUrl": "/proceedings-article/srds/2021/381900a331/1yJZbw6Ti3m",
"parentPublication": {
"id": "proceedings/srds/2021/3819/0",
"title": "2021 40th International Symposium on Reliable Distributed Systems (SRDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/nt/2022/02/09629223",
"title": "ESN Reinforcement Learning for Spectrum and Flight Control in THz-Enabled Drone Networks",
"doi": null,
"abstractUrl": "/journal/nt/2022/02/09629223/1yXvEGg3A5i",
"parentPublication": {
"id": "trans/nt",
"title": "IEEE/ACM Transactions on Networking",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mass/2021/4935/0/493500a278",
"title": "A Novel Internet-of-Drones and Blockchain-based System Architecture for Search and Rescue",
"doi": null,
"abstractUrl": "/proceedings-article/mass/2021/493500a278/1ziOrcSgHKg",
"parentPublication": {
"id": "proceedings/mass/2021/4935/0",
"title": "2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mass/2021/4935/0/493500a446",
"title": "Hybrid Analog-Digital Sensing Approach for Low-power Real-time Anomaly Detection in Drones",
"doi": null,
"abstractUrl": "/proceedings-article/mass/2021/493500a446/1ziOvvKC9X2",
"parentPublication": {
"id": "proceedings/mass/2021/4935/0",
"title": "2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KYsxR5w0RW",
"title": "2023 International Conference on Information Networking (ICOIN)",
"acronym": "icoin",
"groupId": "1000363",
"volume": "0",
"displayVolume": "0",
"year": "2023",
"__typename": "ProceedingType"
},
"article": {
"id": "1KYsUWuEAp2",
"doi": "10.1109/ICOIN56518.2023.10049031",
"title": "UAVs Reformation Approach Based on Packet Loss in GPS-Denied Environments",
"normalizedTitle": "UAVs Reformation Approach Based on Packet Loss in GPS-Denied Environments",
"abstract": "Drone technology, which is employed by a large number of industries and institutions, including the military and commercial, makes use of the Global Positioning System (GPS) to precisely pinpoint locations and has given a huge rise in aerial surveillance applications. Though drones without GPS can still be operated, there is no certainty that the locations are precise due to a lack of real-time position information and existing environmental influences. In this paper, we perform network packet transmission over a drone swarm to relocate imprecise positions of formation when GPS is unavailable. We introduce a reformation approach based on packet loss, which relocates the drone swarm’s formation into the initially intended one to preserve the original coverage area as much as possible. Through simulations, we show that our proposed approach guarantees to acquire the lost coverage area by improving inaccurate formation within a reasonable period of time. Although the adjusted formation is not exactly as originally desired in some cases, the coverage area is still higher obtained than the imprecise one.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Drone technology, which is employed by a large number of industries and institutions, including the military and commercial, makes use of the Global Positioning System (GPS) to precisely pinpoint locations and has given a huge rise in aerial surveillance applications. Though drones without GPS can still be operated, there is no certainty that the locations are precise due to a lack of real-time position information and existing environmental influences. In this paper, we perform network packet transmission over a drone swarm to relocate imprecise positions of formation when GPS is unavailable. We introduce a reformation approach based on packet loss, which relocates the drone swarm’s formation into the initially intended one to preserve the original coverage area as much as possible. Through simulations, we show that our proposed approach guarantees to acquire the lost coverage area by improving inaccurate formation within a reasonable period of time. Although the adjusted formation is not exactly as originally desired in some cases, the coverage area is still higher obtained than the imprecise one.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Drone technology, which is employed by a large number of industries and institutions, including the military and commercial, makes use of the Global Positioning System (GPS) to precisely pinpoint locations and has given a huge rise in aerial surveillance applications. Though drones without GPS can still be operated, there is no certainty that the locations are precise due to a lack of real-time position information and existing environmental influences. In this paper, we perform network packet transmission over a drone swarm to relocate imprecise positions of formation when GPS is unavailable. We introduce a reformation approach based on packet loss, which relocates the drone swarm’s formation into the initially intended one to preserve the original coverage area as much as possible. Through simulations, we show that our proposed approach guarantees to acquire the lost coverage area by improving inaccurate formation within a reasonable period of time. Although the adjusted formation is not exactly as originally desired in some cases, the coverage area is still higher obtained than the imprecise one.",
"fno": "10049031",
"keywords": [
"Aerospace Communication",
"Autonomous Aerial Vehicles",
"Global Positioning System",
"Packet Radio Networks",
"Surveillance",
"Aerial Surveillance Applications",
"Drone Swarm Formation",
"Drone Technology",
"Global Positioning System Denied Environments",
"GPS Denied Environments",
"Network Packet Transmission",
"Packet Loss",
"Real Time Position Information",
"UA Vs Reformation Approach",
"Location Awareness",
"Industries",
"Surveillance",
"Packet Loss",
"Autonomous Aerial Vehicles",
"Real Time Systems",
"Drones",
"Aerial Surveillance",
"Drone Swarm Formation",
"Packet Transmission",
"GPS Denied Environments"
],
"authors": [
{
"affiliation": "Kookmin University,Department of Computer Science,Seoul,South Korea",
"fullName": "Issaree Srisomboon",
"givenName": "Issaree",
"surname": "Srisomboon",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Kookmin University,Department of Computer Science,Seoul,South Korea",
"fullName": "Sanghwan Lee",
"givenName": "Sanghwan",
"surname": "Lee",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icoin",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2023-01-01T00:00:00",
"pubType": "proceedings",
"pages": "606-609",
"year": "2023",
"issn": "1976-7684",
"isbn": "978-1-6654-6268-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "10049046",
"articleId": "1KYsSZl3DhK",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10048901",
"articleId": "1KYsF4VZGAU",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iciibms/2017/6664/0/08279762",
"title": "Verification experiment for drone charging station using RTK-GPS",
"doi": null,
"abstractUrl": "/proceedings-article/iciibms/2017/08279762/12OmNx76TRt",
"parentPublication": {
"id": "proceedings/iciibms/2017/6664/0",
"title": "2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/2023/02/09736583",
"title": "PPCA - Privacy-Preserving Collision Avoidance for Autonomous Unmanned Aerial Vehicles",
"doi": null,
"abstractUrl": "/journal/tq/2023/02/09736583/1BN1Y4DftPW",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09737401",
"title": "Exocentric control scheme for robot applications: An immersive virtual reality approach",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09737401/1BQicsNYBbi",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tm/5555/01/09983486",
"title": "Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs",
"doi": null,
"abstractUrl": "/journal/tm/5555/01/09983486/1J4xW1GasKY",
"parentPublication": {
"id": "trans/tm",
"title": "IEEE Transactions on Mobile Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ises/2022/9922/0/992200a001",
"title": "Classification of UAVs using Time-Frequency Analysis of Remote Control Signals and CNN",
"doi": null,
"abstractUrl": "/proceedings-article/ises/2022/992200a001/1KrgvFnory8",
"parentPublication": {
"id": "proceedings/ises/2022/9922/0",
"title": "2022 IEEE International Symposium on Smart Electronic Systems (iSES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tm/5555/01/10077427",
"title": "Methods to Assign UAVs for K-Coverage and Recharging in IoT Networks",
"doi": null,
"abstractUrl": "/journal/tm/5555/01/10077427/1LFQ1dKczhS",
"parentPublication": {
"id": "trans/tm",
"title": "IEEE Transactions on Mobile Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2023/02/10061588",
"title": "Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows",
"doi": null,
"abstractUrl": "/magazine/ex/2023/02/10061588/1Lk2Nd30JPy",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpads/2020/9074/0/907400a382",
"title": "Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack",
"doi": null,
"abstractUrl": "/proceedings-article/icpads/2020/907400a382/1rvCvTZB7qg",
"parentPublication": {
"id": "proceedings/icpads/2020/9074/0",
"title": "2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/nt/2022/02/09629223",
"title": "ESN Reinforcement Learning for Spectrum and Flight Control in THz-Enabled Drone Networks",
"doi": null,
"abstractUrl": "/journal/nt/2022/02/09629223/1yXvEGg3A5i",
"parentPublication": {
"id": "trans/nt",
"title": "IEEE/ACM Transactions on Networking",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nana/2021/4158/0/415800a501",
"title": "Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems",
"doi": null,
"abstractUrl": "/proceedings-article/nana/2021/415800a501/1zdPK9xSZ7q",
"parentPublication": {
"id": "proceedings/nana/2021/4158/0",
"title": "2021 International Conference on Networking and Network Applications (NaNA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1gyshXRzHpK",
"title": "2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"acronym": "ismar-adjunct",
"groupId": "1810084",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1gysj7RryWk",
"doi": "10.1109/ISMAR-Adjunct.2019.00-54",
"title": "DroneCamo: Modifying Human-Drone Comfort via Augmented Reality",
"normalizedTitle": "DroneCamo: Modifying Human-Drone Comfort via Augmented Reality",
"abstract": "We present an augmented reality (AR) system for drones to improve people's comfort by camouflaging the appearance of the drones via optical see-through AR visualization. Given the growing social use of autonomous robots such as self-driving vehicles and delivery drones, a comfortable coexistence of human and robot is becoming an increasingly important topic. In this work, we explore how AR technology could improve human-robot comfort. Specifically, we demonstrated an AR system and conducted a user study (N=14) on a proof-of-concept AR system consisting of a HoloLens, OptiTrack, and a toy drone. While showing AR avatars with seemingly positive or negative appearances, we collected both objective and subjective measures of the subjects' comfort. Our results show that subjects were more comfortable when the drone was camouflaged with a positive avatar and less comfortable for the negative avatar.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present an augmented reality (AR) system for drones to improve people's comfort by camouflaging the appearance of the drones via optical see-through AR visualization. Given the growing social use of autonomous robots such as self-driving vehicles and delivery drones, a comfortable coexistence of human and robot is becoming an increasingly important topic. In this work, we explore how AR technology could improve human-robot comfort. Specifically, we demonstrated an AR system and conducted a user study (N=14) on a proof-of-concept AR system consisting of a HoloLens, OptiTrack, and a toy drone. While showing AR avatars with seemingly positive or negative appearances, we collected both objective and subjective measures of the subjects' comfort. Our results show that subjects were more comfortable when the drone was camouflaged with a positive avatar and less comfortable for the negative avatar.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present an augmented reality (AR) system for drones to improve people's comfort by camouflaging the appearance of the drones via optical see-through AR visualization. Given the growing social use of autonomous robots such as self-driving vehicles and delivery drones, a comfortable coexistence of human and robot is becoming an increasingly important topic. In this work, we explore how AR technology could improve human-robot comfort. Specifically, we demonstrated an AR system and conducted a user study (N=14) on a proof-of-concept AR system consisting of a HoloLens, OptiTrack, and a toy drone. While showing AR avatars with seemingly positive or negative appearances, we collected both objective and subjective measures of the subjects' comfort. Our results show that subjects were more comfortable when the drone was camouflaged with a positive avatar and less comfortable for the negative avatar.",
"fno": "476500a167",
"keywords": [
"Augmented Reality",
"Autonomous Aerial Vehicles",
"Avatars",
"Data Visualisation",
"Human Factors",
"Human Robot Interaction",
"Mobile Robots",
"Augmented Reality System",
"Autonomous Robots",
"Delivery Drones",
"Human Robot Comfort",
"AR System",
"Toy Drone",
"Negative Appearances",
"Positive Appearances",
"Human Drone Comfort",
"Drone Camo",
"Optical See Through AR Visualization",
"Self Driving Vehicles",
"Holo Lens",
"Opti Track",
"AR Avatars",
"Positive Avatar",
"Negative Avatar",
"Drones",
"Avatars",
"Augmented Reality",
"Autonomous Robots",
"Visualization",
"Current Measurement",
"Computingmethodologies",
"Computergraphics",
"Graphics Systems And Interfaces",
"Mixed Augmented Reality"
],
"authors": [
{
"affiliation": "Tokyo Institute of Technology",
"fullName": "Atsushi Mori",
"givenName": "Atsushi",
"surname": "Mori",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Tokyo Institute of Technology",
"fullName": "Yuta Itoh",
"givenName": "Yuta",
"surname": "Itoh",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ismar-adjunct",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-10-01T00:00:00",
"pubType": "proceedings",
"pages": "167-168",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-4765-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "476500a165",
"articleId": "1gyskfJT9f2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "476500a169",
"articleId": "1gyslq727qU",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icdcs/2017/1792/0/1792a308",
"title": "Networked Drone Cameras for Sports Streaming",
"doi": null,
"abstractUrl": "/proceedings-article/icdcs/2017/1792a308/12OmNwLOYWK",
"parentPublication": {
"id": "proceedings/icdcs/2017/1792/0",
"title": "2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2018/04/08260942",
"title": "Drone-Augmented Human Vision: Exocentric Control for Drones Exploring Hidden Areas",
"doi": null,
"abstractUrl": "/journal/tg/2018/04/08260942/13rRUxcbnHj",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ic/2019/02/08570820",
"title": "Seamless Virtualized Controller Migration for Drone Applications",
"doi": null,
"abstractUrl": "/magazine/ic/2019/02/08570820/17D45X0yjT6",
"parentPublication": {
"id": "mags/ic",
"title": "IEEE Internet Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2018/7592/0/08699182",
"title": "International Workshop on Comfort Intelligence with AR for Autonomous Vehicle 2018",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2018/08699182/19F1R26pH0Y",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2018/7592/0",
"title": "2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2022/5325/0/532500a270",
"title": "DroneARchery: Human-Drone Interaction through Augmented Reality with Haptic Feedback and Multi-UAV Collision Avoidance Driven by Deep Reinforcement Learning",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2022/532500a270/1JrQTgRsONG",
"parentPublication": {
"id": "proceedings/ismar/2022/5325/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/percom-workshops/2019/9151/0/08730844",
"title": "Multi-drone Control with Autonomous Mission Support",
"doi": null,
"abstractUrl": "/proceedings-article/percom-workshops/2019/08730844/1aDSGSWTIQg",
"parentPublication": {
"id": "proceedings/percom-workshops/2019/9151/0",
"title": "2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2019/1377/0/08797893",
"title": "Augmented Reality Interfaces for Semi-Autonomous Drones",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2019/08797893/1cJ0NJAEGQw",
"parentPublication": {
"id": "proceedings/vr/2019/1377/0",
"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/avss/2019/0990/0/08909854",
"title": "Spatio-Temporal Semantic Segmentation for Drone Detection",
"doi": null,
"abstractUrl": "/proceedings-article/avss/2019/08909854/1febLe1qpbO",
"parentPublication": {
"id": "proceedings/avss/2019/0990/0",
"title": "2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2020/7675/0/767500a024",
"title": "Catching the Drone - A Tangible Augmented Reality Game in Superhuman Sports",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2020/767500a024/1pBMeMETmdW",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2020/7675/0",
"title": "2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/irc/2020/5237/0/523700a430",
"title": "Towards Declarative Specification of Multi-Drone BVLOS Missions for UTM",
"doi": null,
"abstractUrl": "/proceedings-article/irc/2020/523700a430/1pP3UDyOnuM",
"parentPublication": {
"id": "proceedings/irc/2020/5237/0",
"title": "2020 Fourth IEEE International Conference on Robotic Computing (IRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1jIxhEnA8IE",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"acronym": "vrw",
"groupId": "1836626",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1jIxv1GXrfa",
"doi": "10.1109/VRW50115.2020.00185",
"title": "Extracting and Transferring Hierarchical Knowledge to Robots using Virtual Reality",
"normalizedTitle": "Extracting and Transferring Hierarchical Knowledge to Robots using Virtual Reality",
"abstract": "We study the knowledge transfer problem by training a task of folding clothes in the virtual world using an Oculus Headset and validating with a physical Baxter robot. We argue such complex transfer is realizable if an abstract graph-based knowledge representation is adopted to facilitate the process. An And-Or-Graph (AOG) grammar model is introduced to represent the knowledge, which can be learned from the human demonstrations performed in the Virtual Reality (VR), followed by the case analysis of folding clothes represented and learned by the AOG grammar model. In the experiment, the learned knowledge from the given six virtual scenarios is implemented on a physical robot platform, demonstrating that the grammar-based knowledge is an effective representation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We study the knowledge transfer problem by training a task of folding clothes in the virtual world using an Oculus Headset and validating with a physical Baxter robot. We argue such complex transfer is realizable if an abstract graph-based knowledge representation is adopted to facilitate the process. An And-Or-Graph (AOG) grammar model is introduced to represent the knowledge, which can be learned from the human demonstrations performed in the Virtual Reality (VR), followed by the case analysis of folding clothes represented and learned by the AOG grammar model. In the experiment, the learned knowledge from the given six virtual scenarios is implemented on a physical robot platform, demonstrating that the grammar-based knowledge is an effective representation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We study the knowledge transfer problem by training a task of folding clothes in the virtual world using an Oculus Headset and validating with a physical Baxter robot. We argue such complex transfer is realizable if an abstract graph-based knowledge representation is adopted to facilitate the process. An And-Or-Graph (AOG) grammar model is introduced to represent the knowledge, which can be learned from the human demonstrations performed in the Virtual Reality (VR), followed by the case analysis of folding clothes represented and learned by the AOG grammar model. In the experiment, the learned knowledge from the given six virtual scenarios is implemented on a physical robot platform, demonstrating that the grammar-based knowledge is an effective representation.",
"fno": "09090512",
"keywords": [
"Robots",
"Virtual Environments",
"Trajectory",
"Physics",
"Knowledge Transfer",
"Training",
"Task Analysis",
"Human Centered Computing",
"Human Computer Interaction HCI",
"Interaction Paradigms",
"Virtual Reality"
],
"authors": [
{
"affiliation": "Tencent",
"fullName": "Zhenliang Zhang",
"givenName": "Zhenliang",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Institute of Technology,Beijing Engineering Research Center of MRAD, School of Optics and Photonics,Beijing,China",
"fullName": "Jie Guo",
"givenName": "Jie",
"surname": "Guo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Institute of Technology,Beijing Engineering Research Center of MRAD, School of Optics and Photonics,Beijing,China",
"fullName": "Dongdong Weng",
"givenName": "Dongdong",
"surname": "Weng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Institute of Technology,Beijing Engineering Research Center of MRAD, School of Optics and Photonics,Beijing,China",
"fullName": "Yue Liu",
"givenName": "Yue",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beijing Institute of Technology,Beijing Engineering Research Center of MRAD, School of Optics and Photonics,Beijing,China",
"fullName": "Yongtian Wang",
"givenName": "Yongtian",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vrw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-03-01T00:00:00",
"pubType": "proceedings",
"pages": "668-669",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-6532-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09090542",
"articleId": "1jIxx2vV5y8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09090590",
"articleId": "1jIxhRmAxBC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/kdex/1997/8230/0/82300054",
"title": "Generating 2-D Space Maps Form Unknown Environments Using Multiple Autonomous Robots",
"doi": null,
"abstractUrl": "/proceedings-article/kdex/1997/82300054/12OmNBr4eKS",
"parentPublication": {
"id": "proceedings/kdex/1997/8230/0",
"title": "Knowledge and Data Exchange, IEEE Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/irc/2017/6724/0/07926532",
"title": "Architecture of an Extensible Visual Programming Environment for Authoring Behaviour of Personal Service Robots",
"doi": null,
"abstractUrl": "/proceedings-article/irc/2017/07926532/12OmNwJPMXB",
"parentPublication": {
"id": "proceedings/irc/2017/6724/0",
"title": "2017 First IEEE International Conference on Robotic Computing (IRC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2014/4308/0/4308a373",
"title": "Semantic Visual Understanding of Indoor Environments: From Structures to Opportunities for Action",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2014/4308a373/12OmNy5hRbo",
"parentPublication": {
"id": "proceedings/cvprw/2014/4308/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "17D45VtKir6",
"title": "2018 24th International Conference on Pattern Recognition (ICPR)",
"acronym": "icpr",
"groupId": "1000545",
"volume": "0",
"displayVolume": "0",
"year": "2018",
"__typename": "ProceedingType"
},
"article": {
"id": "17D45WcjjPY",
"doi": "10.1109/ICPR.2018.8546214",
"title": "Fast Descriptor Extraction for Contextless 3D Registration Using a Fully Convolutional Network",
"normalizedTitle": "Fast Descriptor Extraction for Contextless 3D Registration Using a Fully Convolutional Network",
"abstract": "In recent years, numerous consumer devices have emerged that are capable of capturing 3D point data originating from depth images. Many computer vision tasks, such as object recognition, environment mapping, augmented reality, and more, rely on accurately registering 3D point sets. One method to compute this registration is to use 3D local feature descriptors for a coarse alignment, and to further refine the alignment with a variant of the Iterative Closest Point algorithm. While robust feature descriptors work well for this approach, online computation for all points in a single depth image is typically intractable. In this work, a method to facilitate real-time 3D registration by performing descriptor extraction on depth images using a Fully Convolutional Network (FCN) is presented. The input to this method is a raw depth image and results in a 33-bin descriptor for each pixel, enabling a general-purpose 3D registration process that doesn't require future network retraining and refinement. Experimental results on consumer hardware demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of computation time and approaches depth sensor frame capture times, with only a slight reduction in descriptiveness.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In recent years, numerous consumer devices have emerged that are capable of capturing 3D point data originating from depth images. Many computer vision tasks, such as object recognition, environment mapping, augmented reality, and more, rely on accurately registering 3D point sets. One method to compute this registration is to use 3D local feature descriptors for a coarse alignment, and to further refine the alignment with a variant of the Iterative Closest Point algorithm. While robust feature descriptors work well for this approach, online computation for all points in a single depth image is typically intractable. In this work, a method to facilitate real-time 3D registration by performing descriptor extraction on depth images using a Fully Convolutional Network (FCN) is presented. The input to this method is a raw depth image and results in a 33-bin descriptor for each pixel, enabling a general-purpose 3D registration process that doesn't require future network retraining and refinement. Experimental results on consumer hardware demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of computation time and approaches depth sensor frame capture times, with only a slight reduction in descriptiveness.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In recent years, numerous consumer devices have emerged that are capable of capturing 3D point data originating from depth images. Many computer vision tasks, such as object recognition, environment mapping, augmented reality, and more, rely on accurately registering 3D point sets. One method to compute this registration is to use 3D local feature descriptors for a coarse alignment, and to further refine the alignment with a variant of the Iterative Closest Point algorithm. While robust feature descriptors work well for this approach, online computation for all points in a single depth image is typically intractable. In this work, a method to facilitate real-time 3D registration by performing descriptor extraction on depth images using a Fully Convolutional Network (FCN) is presented. The input to this method is a raw depth image and results in a 33-bin descriptor for each pixel, enabling a general-purpose 3D registration process that doesn't require future network retraining and refinement. Experimental results on consumer hardware demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of computation time and approaches depth sensor frame capture times, with only a slight reduction in descriptiveness.",
"fno": "08546214",
"keywords": [
"Three Dimensional Displays",
"Feature Extraction",
"Hardware",
"Task Analysis",
"Object Recognition",
"Iterative Closest Point Algorithm",
"Training"
],
"authors": [
{
"affiliation": "Virtual Reality Applications Center, Iowa State University, Ames, Iowa, USA",
"fullName": "Timothy Garrett",
"givenName": "Timothy",
"surname": "Garrett",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Virtual Reality Applications Center, Iowa State University, Ames, Iowa, USA",
"fullName": "Rafael Radkowski",
"givenName": "Rafael",
"surname": "Radkowski",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2018-08-01T00:00:00",
"pubType": "proceedings",
"pages": "1211-1216",
"year": "2018",
"issn": "1051-4651",
"isbn": "978-1-5386-3788-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "08545389",
"articleId": "17D45WHONrs",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08545100",
"articleId": "17D45VWpMzg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccvw/2009/4442/0/05457428",
"title": "Elastic convolved ICP for the registration of deformable objects",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2009/05457428/12OmNBEGYFD",
"parentPublication": {
"id": "proceedings/iccvw/2009/4442/0",
"title": "2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2015/6683/0/6683a094",
"title": "Non-rigid Articulated Point Set Registration for Human Pose Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2015/6683a094/12OmNC2OSPb",
"parentPublication": {
"id": "proceedings/wacv/2015/6683/0",
"title": "2015 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2008/2153/0/04813359",
"title": "Component-based registration with curvature descriptors for expression insensitive 3d face recognition",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2008/04813359/12OmNrYCXSl",
"parentPublication": {
"id": "proceedings/fg/2008/2153/0",
"title": "2008 8th IEEE International Conference on Automatic Face & Gesture Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/crv/2014/4337/0/4337a077",
"title": "3D Scan Registration Using Curvelet Features",
"doi": null,
"abstractUrl": "/proceedings-article/crv/2014/4337a077/12OmNrYCXVo",
"parentPublication": {
"id": "proceedings/crv/2014/4337/0",
"title": "2014 Canadian Conference on Computer and Robot Vision (CRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2006/2597/1/01640899",
"title": "Fully Automatic Registration of 3D Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2006/01640899/12OmNscOUbU",
"parentPublication": {
"id": "proceedings/cvpr/2006/2597/2",
"title": "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2017/0457/0/0457c472",
"title": "3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457c472/12OmNwDSdyQ",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2009/4420/0/05459321",
"title": "Coarse registration of 3D surface triangulations based on moment invariants with applications to object alignment and identification",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2009/05459321/12OmNxcMSjB",
"parentPublication": {
"id": "proceedings/iccv/2009/4420/0",
"title": "2009 IEEE 12th International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/2012/4829/0/4829a047",
"title": "Appearance and Geometry Fusion for Enhanced Dense 3D Alignment",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2012/4829a047/12OmNyaGeFm",
"parentPublication": {
"id": "proceedings/sibgrapi/2012/4829/0",
"title": "2012 25th SIBGRAPI Conference on Graphics, Patterns and Images",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2018/8425/0/842500a756",
"title": "NRGA: Gravitational Approach for Non-rigid Point Set Registration",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2018/842500a756/17D45XwUAHp",
"parentPublication": {
"id": "proceedings/3dv/2018/8425/0",
"title": "2018 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09736452",
"title": "WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09736452/1BN1Ujkoysg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1H1gVMlkl32",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1H1k2YPLPTG",
"doi": "10.1109/CVPR52688.2022.01451",
"title": "HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization",
"normalizedTitle": "HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization",
"abstract": "To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is the first framework to leverage both point consistency and employ contrastive regularization with pseudo labeling in an end-to-end manner. Fundamentally, HybridCR explicitly and effectively considers the semantic similarity between local neighboring points and global characteristics of 3D classes. We further design a dynamic point cloud augmentor to generate diversity and robust sample views, whose transformation parameter is jointly optimized with model training. Through extensive experiments, HybridCR achieves significant performance improvement against the SOTA methods on both indoor and outdoor datasets, e.g., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI.",
"abstracts": [
{
"abstractType": "Regular",
"content": "To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is the first framework to leverage both point consistency and employ contrastive regularization with pseudo labeling in an end-to-end manner. Fundamentally, HybridCR explicitly and effectively considers the semantic similarity between local neighboring points and global characteristics of 3D classes. We further design a dynamic point cloud augmentor to generate diversity and robust sample views, whose transformation parameter is jointly optimized with model training. Through extensive experiments, HybridCR achieves significant performance improvement against the SOTA methods on both indoor and outdoor datasets, e.g., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is the first framework to leverage both point consistency and employ contrastive regularization with pseudo labeling in an end-to-end manner. Fundamentally, HybridCR explicitly and effectively considers the semantic similarity between local neighboring points and global characteristics of 3D classes. We further design a dynamic point cloud augmentor to generate diversity and robust sample views, whose transformation parameter is jointly optimized with model training. Through extensive experiments, HybridCR achieves significant performance improvement against the SOTA methods on both indoor and outdoor datasets, e.g., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI.",
"fno": "694600o4910",
"keywords": [
"Computational Geometry",
"Image Segmentation",
"Stereo Image Processing",
"Supervised Learning",
"Dynamic Point Cloud Augmentor",
"Large Scale Point Cloud Semantic Segmentation",
"Weakly Supervised Setting",
"Pseudolabeling",
"Hybrid CR",
"Point Consistency",
"Hybrid Contrastive Regularization",
"Weakly Supervised 3 D Point Cloud Semantic Segmentation",
"S 3 DIS Dataset",
"Scan Net V 2 Dataset",
"Semantic 3 D Dataset",
"Semantic KITTI Dataset",
"Point Cloud Compression",
"Training",
"Computer Vision",
"Three Dimensional Displays",
"Costs",
"Shape",
"Computational Modeling",
"Computer Vision For Social Good Segmentation",
"Grouping And Shape Analysis Self X 0026 Semi X 0026 Meta Transfer Low Shot Long Tail Learning"
],
"authors": [
{
"affiliation": "School of Computer Science and Technology, East China Normal University,Shanghai,China",
"fullName": "Mengtian Li",
"givenName": "Mengtian",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computer Science and Technology, East China Normal University,Shanghai,China",
"fullName": "Yuan Xie",
"givenName": "Yuan",
"surname": "Xie",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Tencent Youtu Lab",
"fullName": "Yunhang Shen",
"givenName": "Yunhang",
"surname": "Shen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Tencent Youtu Lab",
"fullName": "Bo Ke",
"givenName": "Bo",
"surname": "Ke",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Tencent Youtu Lab",
"fullName": "Ruizhi Qiao",
"givenName": "Ruizhi",
"surname": "Qiao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Tencent Youtu Lab",
"fullName": "Bo Ren",
"givenName": "Bo",
"surname": "Ren",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computer Science and Technology, East China Normal University,Shanghai,China",
"fullName": "Shaohui Lin",
"givenName": "Shaohui",
"surname": "Lin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computer Science and Technology, East China Normal University,Shanghai,China",
"fullName": "Lizhuang Ma",
"givenName": "Lizhuang",
"surname": "Ma",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-06-01T00:00:00",
"pubType": "proceedings",
"pages": "14910-14919",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6946-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1H1k2VwPrZ6",
"name": "pcvpr202269460-09879771s1-mm_694600o4910.zip",
"size": "2.77 MB",
"location": "https://www.computer.org/csdl/api/v1/extra/pcvpr202269460-09879771s1-mm_694600o4910.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "694600o4900",
"articleId": "1H1lQVg6sGQ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "694600o4920",
"articleId": "1H1jbCIyoik",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200g403",
"title": "Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g403/1BmEACglKnu",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200j599",
"title": "SelfReg: Self-supervised Contrastive Regularization for Domain Generalization",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200j599/1BmGheOWboQ",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200p5500",
"title": "Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5500/1BmIpda1F8Q",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200k0022",
"title": "Weakly Supervised Contrastive Learning",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200k0022/1BmKqySVuUw",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200h315",
"title": "Unsupervised Point Cloud Object Co-segmentation by Co-contrastive Learning and Mutual Attention Sampling",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h315/1BmLfkQYoz6",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2022/8739/0/873900d910",
"title": "Contrastive Regularization for Semi-Supervised Learning",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2022/873900d910/1G578NN3eyQ",
"parentPublication": {
"id": "proceedings/cvprw/2022/8739/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600i479",
"title": "Contrastive Boundary Learning for Point Cloud Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600i479/1H0KKjoK72U",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600j892",
"title": "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600j892/1H0L5HmKF0c",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1820",
"title": "An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1820/1H1hJaELWaA",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1830",
"title": "Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1830/1H1kfGGzKtW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1H1gVMlkl32",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
},
"article": {
"id": "1H1kfGGzKtW",
"doi": "10.1109/CVPR52688.2022.01154",
"title": "Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation",
"normalizedTitle": "Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation",
"abstract": "Existing point cloud segmentation methods require a large amount of annotated data, especially for the outdoor point cloud scene. Due to the complexity of the outdoor 3D scenes, manual annotations on the outdoor point cloud scene are time-consuming and expensive. In this paper, we study how to achieve scene understanding with limited annotated data. Treating 100 consecutive frames as a sequence, we divide the whole dataset into a series of sequences and annotate only 0.1% points in the first frame of each sequence to reduce the annotation requirements. This leads to a total annotation budget of 0.001%. We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data. Specifically, the frame-work contains two modules: an matching module in temporal dimension to propagate pseudo labels across different frames, and a graph propagation module in spatial dimension to propagate the information of pseudo labels to the entire point clouds in each frame. With only 0.001% annotations for training, experimental results on both SemanticKITTI and SemanticPOSS shows our weakly supervised two-stage framework is comparable to some existing fully supervised methods. We also evaluate our framework with 0.005% initial annotations on SemanticKITTI, and achieve a result close to fully supervised backbone model.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Existing point cloud segmentation methods require a large amount of annotated data, especially for the outdoor point cloud scene. Due to the complexity of the outdoor 3D scenes, manual annotations on the outdoor point cloud scene are time-consuming and expensive. In this paper, we study how to achieve scene understanding with limited annotated data. Treating 100 consecutive frames as a sequence, we divide the whole dataset into a series of sequences and annotate only 0.1% points in the first frame of each sequence to reduce the annotation requirements. This leads to a total annotation budget of 0.001%. We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data. Specifically, the frame-work contains two modules: an matching module in temporal dimension to propagate pseudo labels across different frames, and a graph propagation module in spatial dimension to propagate the information of pseudo labels to the entire point clouds in each frame. With only 0.001% annotations for training, experimental results on both SemanticKITTI and SemanticPOSS shows our weakly supervised two-stage framework is comparable to some existing fully supervised methods. We also evaluate our framework with 0.005% initial annotations on SemanticKITTI, and achieve a result close to fully supervised backbone model.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Existing point cloud segmentation methods require a large amount of annotated data, especially for the outdoor point cloud scene. Due to the complexity of the outdoor 3D scenes, manual annotations on the outdoor point cloud scene are time-consuming and expensive. In this paper, we study how to achieve scene understanding with limited annotated data. Treating 100 consecutive frames as a sequence, we divide the whole dataset into a series of sequences and annotate only 0.1% points in the first frame of each sequence to reduce the annotation requirements. This leads to a total annotation budget of 0.001%. We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data. Specifically, the frame-work contains two modules: an matching module in temporal dimension to propagate pseudo labels across different frames, and a graph propagation module in spatial dimension to propagate the information of pseudo labels to the entire point clouds in each frame. With only 0.001% annotations for training, experimental results on both SemanticKITTI and SemanticPOSS shows our weakly supervised two-stage framework is comparable to some existing fully supervised methods. We also evaluate our framework with 0.005% initial annotations on SemanticKITTI, and achieve a result close to fully supervised backbone model.",
"fno": "694600l1830",
"keywords": [
"Geophysical Image Processing",
"Image Segmentation",
"Learning Artificial Intelligence",
"0 005 Initial Annotations",
"Supervised Segmentation",
"Outdoor 4 D Point Clouds",
"Temporal Matching",
"Spatial Graph Propagation",
"Point Cloud Segmentation Methods",
"Annotated Data",
"Outdoor Point Cloud Scene",
"Outdoor 3 D Scenes",
"Manual Annotations",
"Scene Understanding",
"100 Consecutive Frames",
"Annotation Requirements",
"Total Annotation Budget",
"Novel Temporal Spatial Framework",
"Effective Weakly Supervised Learning",
"High Quality Pseudolabels",
"Graph Propagation Module",
"Entire Point Clouds",
"Point Cloud Compression",
"Training",
"Solid Modeling",
"Computer Vision",
"Three Dimensional Displays",
"Annotations",
"Computational Modeling",
"Segmentation",
"Grouping And Shape Analysis 3 D From Multi View And Sensors Image And Video Synthesis And Generation Scene Analysis And Understanding Self Amp Semi Amp Meta Video Analysis And Understanding"
],
"authors": [
{
"affiliation": "Nanyang Technological University",
"fullName": "Hanyu Shi",
"givenName": "Hanyu",
"surname": "Shi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanyang Technological University",
"fullName": "Jiacheng Wei",
"givenName": "Jiacheng",
"surname": "Wei",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanyang Technological University",
"fullName": "Ruibo Li",
"givenName": "Ruibo",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Institute for Infocomm Research, A*STAR",
"fullName": "Fayao Liu",
"givenName": "Fayao",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanyang Technological University",
"fullName": "Guosheng Lin",
"givenName": "Guosheng",
"surname": "Lin",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2022-06-01T00:00:00",
"pubType": "proceedings",
"pages": "11830-11839",
"year": "2022",
"issn": null,
"isbn": "978-1-6654-6946-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1H1kfDzQ7a8",
"name": "pcvpr202269460-09879977s1-mm_694600l1830.zip",
"size": "434 kB",
"location": "https://www.computer.org/csdl/api/v1/extra/pcvpr202269460-09879977s1-mm_694600l1830.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "694600l1820",
"articleId": "1H1hJaELWaA",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "694600l1840",
"articleId": "1H1hCbGOBbi",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200g403",
"title": "Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g403/1BmEACglKnu",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200g515",
"title": "Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g515/1BmHreVQrSg",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200p5490",
"title": "ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5490/1BmLgVFEs3C",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600o4799",
"title": "3DAC: Learning Attribute Compression for Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600o4799/1H0LpmYzOeI",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600c687",
"title": "Scribble-Supervised LiDAR Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600c687/1H1hDMLXody",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600o4910",
"title": "HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600o4910/1H1k2YPLPTG",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956238",
"title": "HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point Cloud Processing",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956238/1IHpg8unl4c",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/9.346E265",
"title": "Weakly-supervised Point Cloud Instance Segmentation with Geometric Priors",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/9.346E265/1KxVbnA0Z5S",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10039501",
"title": "Salvage of Supervision in Weakly Supervised Object Detection and Segmentation",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10039501/1KzA0tkWurK",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600b644",
"title": "Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600b644/1L8qrCqPxPq",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KxUhhFgzlK",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"acronym": "wacv",
"groupId": "1000040",
"volume": "0",
"displayVolume": "0",
"year": "2023",
"__typename": "ProceedingType"
},
"article": {
"id": "1KxVbnA0Z5S",
"doi": "10.1109/WACV56688.2023.00425",
"title": "Weakly-supervised Point Cloud Instance Segmentation with Geometric Priors",
"normalizedTitle": "Weakly-supervised Point Cloud Instance Segmentation with Geometric Priors",
"abstract": "This paper investigates how to leverage more readily acquired annotations, i.e., 3D bounding boxes instead of dense point-wise labels, for instance segmentation. We propose a Weakly-supervised point cloud Instance Segmentation framework with Geometric Priors (WISGP) that allows segmentation models to be trained with 3D bounding boxes of instances. Considering intersections among bounding boxes in a scene would result in ambiguous la- bels, we first group points into two sets, i.e., univocal and equivocal sets, indicating the certainty of a 3D point belonging to an instance, respectively. Specifically, 3D points with clear labels belong to the univocal set while the rest are grouped into the equivocal set. To assign reliable labels to points in the equivocal set, we design a Geometry-guided Label Propagation (GLP) scheme that progressively propagates labels to linked points based on geometric structure, e.g., polygon meshes and superpoints. Afterwards, we train an instance segmentation model with the univocal points and equivocal points labeled by GLP, and then employ it to assign pseudo labels for the remainder of the unlabeled points. Lastly, we retrain the model with all the labeled points to achieve better instance segmentation performance. Experiments on large-scale datasets ScanNet-v2 and S3DIS demonstrate that WISGP is superior to competing weakly-supervised algorithms and even on par with a few fully-supervised ones.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper investigates how to leverage more readily acquired annotations, i.e., 3D bounding boxes instead of dense point-wise labels, for instance segmentation. We propose a Weakly-supervised point cloud Instance Segmentation framework with Geometric Priors (WISGP) that allows segmentation models to be trained with 3D bounding boxes of instances. Considering intersections among bounding boxes in a scene would result in ambiguous la- bels, we first group points into two sets, i.e., univocal and equivocal sets, indicating the certainty of a 3D point belonging to an instance, respectively. Specifically, 3D points with clear labels belong to the univocal set while the rest are grouped into the equivocal set. To assign reliable labels to points in the equivocal set, we design a Geometry-guided Label Propagation (GLP) scheme that progressively propagates labels to linked points based on geometric structure, e.g., polygon meshes and superpoints. Afterwards, we train an instance segmentation model with the univocal points and equivocal points labeled by GLP, and then employ it to assign pseudo labels for the remainder of the unlabeled points. Lastly, we retrain the model with all the labeled points to achieve better instance segmentation performance. Experiments on large-scale datasets ScanNet-v2 and S3DIS demonstrate that WISGP is superior to competing weakly-supervised algorithms and even on par with a few fully-supervised ones.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper investigates how to leverage more readily acquired annotations, i.e., 3D bounding boxes instead of dense point-wise labels, for instance segmentation. We propose a Weakly-supervised point cloud Instance Segmentation framework with Geometric Priors (WISGP) that allows segmentation models to be trained with 3D bounding boxes of instances. Considering intersections among bounding boxes in a scene would result in ambiguous la- bels, we first group points into two sets, i.e., univocal and equivocal sets, indicating the certainty of a 3D point belonging to an instance, respectively. Specifically, 3D points with clear labels belong to the univocal set while the rest are grouped into the equivocal set. To assign reliable labels to points in the equivocal set, we design a Geometry-guided Label Propagation (GLP) scheme that progressively propagates labels to linked points based on geometric structure, e.g., polygon meshes and superpoints. Afterwards, we train an instance segmentation model with the univocal points and equivocal points labeled by GLP, and then employ it to assign pseudo labels for the remainder of the unlabeled points. Lastly, we retrain the model with all the labeled points to achieve better instance segmentation performance. Experiments on large-scale datasets ScanNet-v2 and S3DIS demonstrate that WISGP is superior to competing weakly-supervised algorithms and even on par with a few fully-supervised ones.",
"fno": "9.346E265",
"keywords": [
"Feature Extraction",
"Geometry",
"Image Segmentation",
"Object Detection",
"Supervised Learning",
"3 D Bounding Boxes",
"Clear Labels",
"Dense Point Wise Labels",
"Equivocal Points",
"Equivocal Set",
"Equivocal Sets",
"Geometric Priors",
"Geometry Guided Label Propagation Scheme",
"Group Points",
"Instance Segmentation Model",
"Instance Segmentation Performance",
"Labeled Points",
"Leverage More Readily Acquired Annotations",
"Pseudolabels",
"Reliable Labels",
"S 3 DIS Demonstrate",
"Segmentation Models",
"Univocal Points",
"Univocal Set",
"Univocal Sets",
"Unlabeled Points",
"Weakly Supervised Algorithms",
"Weakly Supervised Point Cloud Instance Segmentation Framework",
"Point Cloud Compression",
"Solid Modeling",
"Computer Vision",
"Three Dimensional Displays",
"Annotations",
"Computational Modeling",
"Semantics"
],
"authors": [
{
"affiliation": "Australian National University",
"fullName": "Heming Du",
"givenName": "Heming",
"surname": "Du",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Technology Sydney",
"fullName": "Xin Yu",
"givenName": "Xin",
"surname": "Yu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Technology Sydney",
"fullName": "Farookh Hussain",
"givenName": "Farookh",
"surname": "Hussain",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Data61, CSIRO",
"fullName": "Mohammad Ali Armin",
"givenName": "Mohammad Ali",
"surname": "Armin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Data61, CSIRO",
"fullName": "Lars Petersson",
"givenName": "Lars",
"surname": "Petersson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Data61, CSIRO",
"fullName": "Weihao Li",
"givenName": "Weihao",
"surname": "Li",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wacv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2023-01-01T00:00:00",
"pubType": "proceedings",
"pages": "4260-4269",
"year": "2023",
"issn": null,
"isbn": "978-1-6654-9346-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "9.346E254",
"articleId": "1KxVNbGUjqE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "9.346E275",
"articleId": "1KxVDKoBiEM",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2017/0457/0/0457b665",
"title": "Simple Does It: Weakly Supervised Instance and Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457b665/12OmNxeusYt",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200i178",
"title": "Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200i178/1BmFS3A1c88",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/6.946E273",
"title": "Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/6.946E273/1H0KZg9VpaE",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600c607",
"title": "Pointly-Supervised Instance Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600c607/1H1k04bdWlW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1830",
"title": "Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1830/1H1kfGGzKtW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300c204",
"title": "Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300c204/1gyrC1wzTqg",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300i819",
"title": "JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300i819/1gysefJqNaM",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09312978",
"title": "Weakly Supervised Instance Segmentation of SEM Image via Synthetic Data",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09312978/1qmfPqMMuNG",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/12/09628055",
"title": "Point Cloud Instance Segmentation With Semi-Supervised Bounding-Box Mining",
"doi": null,
"abstractUrl": "/journal/tp/2022/12/09628055/1yXvHFG5U2c",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900n3963",
"title": "Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900n3963/1yeLZCLmfDi",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1KxUhhFgzlK",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"acronym": "wacv",
"groupId": "1000040",
"volume": "0",
"displayVolume": "0",
"year": "2023",
"__typename": "ProceedingType"
},
"article": {
"id": "1KxVfZiYZOg",
"doi": "10.1109/WACV56688.2023.00065",
"title": "GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation",
"normalizedTitle": "GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation",
"abstract": "While point cloud semantic segmentation is a significant task in 3D scene understanding, this task demands a time-consuming process of fully annotating labels. To address this problem, recent studies adopt a weakly supervised learning approach under the sparse annotation. Different from the existing studies, this study aims to reduce the epistemic uncertainty measured by the entropy for a precise semantic segmentation. We propose the graphical information gain based attention network called GaIA, which alleviates the entropy of each point based on the reliable information. The graphical information gain discriminates the reliable point by employing relative entropy between target point and its neighborhoods. We further introduce anchor-based additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled points containing high entropy towards semantically similar classes of the labeled points on hypersphere space. Experimental results on S3DIS and ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly supervised methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "While point cloud semantic segmentation is a significant task in 3D scene understanding, this task demands a time-consuming process of fully annotating labels. To address this problem, recent studies adopt a weakly supervised learning approach under the sparse annotation. Different from the existing studies, this study aims to reduce the epistemic uncertainty measured by the entropy for a precise semantic segmentation. We propose the graphical information gain based attention network called GaIA, which alleviates the entropy of each point based on the reliable information. The graphical information gain discriminates the reliable point by employing relative entropy between target point and its neighborhoods. We further introduce anchor-based additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled points containing high entropy towards semantically similar classes of the labeled points on hypersphere space. Experimental results on S3DIS and ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly supervised methods.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "While point cloud semantic segmentation is a significant task in 3D scene understanding, this task demands a time-consuming process of fully annotating labels. To address this problem, recent studies adopt a weakly supervised learning approach under the sparse annotation. Different from the existing studies, this study aims to reduce the epistemic uncertainty measured by the entropy for a precise semantic segmentation. We propose the graphical information gain based attention network called GaIA, which alleviates the entropy of each point based on the reliable information. The graphical information gain discriminates the reliable point by employing relative entropy between target point and its neighborhoods. We further introduce anchor-based additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled points containing high entropy towards semantically similar classes of the labeled points on hypersphere space. Experimental results on S3DIS and ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly supervised methods.",
"fno": "934600a582",
"keywords": [
"Entropy",
"Feature Extraction",
"Image Segmentation",
"Learning Artificial Intelligence",
"Supervised Learning",
"3 D Scene Understanding",
"Anchor Based Additive Angular Margin Loss",
"Existing Weakly Supervised Methods",
"Ga IA",
"Graphical Information Gain Based Attention Network",
"High Entropy",
"Labeled Points",
"Precise Semantic Segmentation",
"Relative Entropy",
"Reliable Information",
"Reliable Point",
"Semantically Similar Classes",
"Sparse Annotation",
"Target Point",
"Time Consuming Process",
"Unlabeled Points",
"Weakly Supervised Learning Approach",
"Weakly Supervised Point Cloud Semantic Segmentation",
"Point Cloud Compression",
"Uncertainty",
"Additives",
"Three Dimensional Displays",
"Semantic Segmentation",
"Computer Network Reliability",
"Supervised Learning",
"Algorithms 3 D Computer Vision",
"Image Recognition And Understanding Object Detection",
"Categorization",
"Segmentation",
"Scene Modeling",
"Visual Reasoning"
],
"authors": [
{
"affiliation": "Korea University,School of Industrial and Management Engineering",
"fullName": "Min Seok Lee",
"givenName": "Min Seok",
"surname": "Lee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Korea University,School of Industrial and Management Engineering",
"fullName": "Seok Woo Yang",
"givenName": "Seok",
"surname": "Woo Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Korea University,School of Industrial and Management Engineering",
"fullName": "Sung Won Han",
"givenName": "Sung Won",
"surname": "Han",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wacv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2023-01-01T00:00:00",
"pubType": "proceedings",
"pages": "582-591",
"year": "2023",
"issn": null,
"isbn": "978-1-6654-9346-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1KxVfVXcCB2",
"name": "pwacv202393460-010030243s1-mm_934600a582.zip",
"size": "4.99 MB",
"location": "https://www.computer.org/csdl/api/v1/extra/pwacv202393460-010030243s1-mm_934600a582.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "934600a572",
"articleId": "1L8qEq48PHa",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "934600a592",
"articleId": "1L8qlBdr5q8",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tg/5555/01/09736452",
"title": "WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09736452/1BN1Ujkoysg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200p5500",
"title": "Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5500/1BmIpda1F8Q",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1820",
"title": "An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1820/1H1hJaELWaA",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600a794",
"title": "Revisiting Weakly Supervised Pre-Training of Visual Perception Models",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600a794/1H1jc9GRt4Y",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600o4910",
"title": "HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600o4910/1H1k2YPLPTG",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1830",
"title": "Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1830/1H1kfGGzKtW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956526",
"title": "Average Activation Network for Weakly Supervised Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956526/1IHoMczyxMY",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956415",
"title": "MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956415/1IHpq6TdNoA",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2022/5670/0/567000a505",
"title": "Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2022/567000a505/1KYsn5aw9FK",
"parentPublication": {
"id": "proceedings/3dv/2022/5670/0",
"title": "2022 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/9.346E265",
"title": "Weakly-supervised Point Cloud Instance Segmentation with Geometric Priors",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/9.346E265/1KxVbnA0Z5S",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1yeHGyRsuys",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1yeMfLYgNS8",
"doi": "10.1109/CVPR46437.2021.01158",
"title": "SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration",
"normalizedTitle": "SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration",
"abstract": "Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.",
"fno": "450900l1748",
"keywords": [
"Feature Extraction",
"Image Registration",
"Learning Artificial Intelligence",
"Neural Nets",
"Optimisation",
"Representative Descriptor",
"Generalization Ability",
"General Surface Descriptor",
"3 D Point Cloud Registration",
"Local Features",
"Downstream Tasks",
"Learning Based Local Descriptors",
"Rotation Transformations",
"Classical Handcrafted Features",
"Neural Architecture",
"Spin Net",
"Spatial Point Transformer",
"Input Local Surface",
"Carefully Designed Cylindrical Space",
"End To End Optimization",
"Neural Feature Extractor",
"3 D Cylindrical Convolutional Neural Layers",
"Compact Descriptor",
"Powerful Point Based Convolutional Neural Layers",
"Convolutional Codes",
"Computer Vision",
"Three Dimensional Displays",
"Computer Architecture",
"Detectors",
"Feature Extraction",
"Transformers"
],
"authors": [
{
"affiliation": "Sun Yat-sen University",
"fullName": "Sheng Ao",
"givenName": "Sheng",
"surname": "Ao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Oxford",
"fullName": "Qingyong Hu",
"givenName": "Qingyong",
"surname": "Hu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Hong Kong Polytechnic University",
"fullName": "Bo Yang",
"givenName": "Bo",
"surname": "Yang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Oxford",
"fullName": "Andrew Markham",
"givenName": "Andrew",
"surname": "Markham",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Sun Yat-sen University",
"fullName": "Yulan Guo",
"givenName": "Yulan",
"surname": "Guo",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-06-01T00:00:00",
"pubType": "proceedings",
"pages": "11748-11757",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-4509-2",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [
{
"id": "1yeMfDpKvLy",
"name": "pcvpr202145090-09577271s1-mm_450900l1748.zip",
"size": "14.9 MB",
"location": "https://www.computer.org/csdl/api/v1/extra/pcvpr202145090-09577271s1-mm_450900l1748.zip",
"__typename": "WebExtraType"
}
],
"adjacentArticles": {
"previous": {
"fno": "450900l1738",
"articleId": "1yeIugGoSUU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "450900l1758",
"articleId": "1yeLegPnV2E",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "trans/tg/5555/01/09736452",
"title": "WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09736452/1BN1Ujkoysg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200p5273",
"title": "LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5273/1BmIQQNgI12",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/03/09792207",
"title": "You Only Train Once: Learning General and Distinctive 3D Local Descriptors",
"doi": null,
"abstractUrl": "/journal/tp/2023/03/09792207/1E5LAGI5KXC",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/05/09878213",
"title": "Robust Point Cloud Registration Framework Based on Deep Graph Matching",
"doi": null,
"abstractUrl": "/journal/tp/2023/05/09878213/1GrP5OekHDy",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1133",
"title": "Geometric Transformer for Fast and Robust Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1133/1H0NxQCVYxW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600u0803",
"title": "Affine Medical Image Registration with Coarse-to-Fine Vision Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600u0803/1H1mWoX1OcE",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900p5854",
"title": "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900p5854/1yeHPco0aiY",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/4.509E270",
"title": "PREDATOR: Registration of 3D Point Clouds with Low Overlap",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/4.509E270/1yeIk4YXHJ6",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900i889",
"title": "Robust Point Cloud Registration Framework Based on Deep Graph Matching",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900i889/1yeJnETcRR6",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900p5955",
"title": "DeepI2P: Image-to-Point Cloud Registration via Deep Classification",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900p5955/1yeLi36Hp84",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1zWE36wtuCY",
"title": "2021 International Conference on 3D Vision (3DV)",
"acronym": "3dv",
"groupId": "1800494",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1zWEfYNkrg4",
"doi": "10.1109/3DV53792.2021.00142",
"title": "3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning",
"normalizedTitle": "3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning",
"abstract": "We propose a method for generalizing deep learning for 3D point cloud registration on new, totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Multi-Scale Sparse Voxel Convolution, MSSVConv is a fast deep neural network that outputs the descriptors from point clouds for 3D registration between two scenes. UDGE is an algorithm for transferring deep networks on unknown datasets in a unsupervised way. The interest of the proposed method appears while using the two components, MS-SVConv and UDGE, together as a whole, which leads to state-of-the-art results on real world registration datasets such as 3DMatch, ETH and TUM. The code is publicly available at https://github.com/humanpose1/MS-SVConv.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We propose a method for generalizing deep learning for 3D point cloud registration on new, totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Multi-Scale Sparse Voxel Convolution, MSSVConv is a fast deep neural network that outputs the descriptors from point clouds for 3D registration between two scenes. UDGE is an algorithm for transferring deep networks on unknown datasets in a unsupervised way. The interest of the proposed method appears while using the two components, MS-SVConv and UDGE, together as a whole, which leads to state-of-the-art results on real world registration datasets such as 3DMatch, ETH and TUM. The code is publicly available at https://github.com/humanpose1/MS-SVConv.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We propose a method for generalizing deep learning for 3D point cloud registration on new, totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Multi-Scale Sparse Voxel Convolution, MSSVConv is a fast deep neural network that outputs the descriptors from point clouds for 3D registration between two scenes. UDGE is an algorithm for transferring deep networks on unknown datasets in a unsupervised way. The interest of the proposed method appears while using the two components, MS-SVConv and UDGE, together as a whole, which leads to state-of-the-art results on real world registration datasets such as 3DMatch, ETH and TUM. The code is publicly available at https://github.com/humanpose1/MS-SVConv.",
"fno": "268800b351",
"keywords": [
"Convolutional Neural Nets",
"Deep Learning Artificial Intelligence",
"Image Registration",
"Neural Net Architecture",
"Solid Modelling",
"Stereo Image Processing",
"Unsupervised Learning",
"UDGE",
"3 D Point Cloud Registration",
"Deep Learning",
"Deep Neural Network",
"Multiscale Sparse Voxel Convolution",
"Multiscale Architecture",
"Unsupervised Transfer Learning",
"MS SV Conv",
"Point Cloud Compression",
"Deep Learning",
"Three Dimensional Displays",
"Codes",
"Convolution",
"Transfer Learning",
"Supervised Learning",
"Deep Learning",
"3 D Point Cloud",
"Registration"
],
"authors": [
{
"affiliation": "PSL University, Centre for Robotics,MINES ParisTech,Paris,France,75006",
"fullName": "Sofiane Horache",
"givenName": "Sofiane",
"surname": "Horache",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "PSL University, Centre for Robotics,MINES ParisTech,Paris,France,75006",
"fullName": "Jean-Emmanuel Deschaud",
"givenName": "Jean-Emmanuel",
"surname": "Deschaud",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "PSL University, Centre for Robotics,MINES ParisTech,Paris,France,75006",
"fullName": "François Goulette",
"givenName": "François",
"surname": "Goulette",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "3dv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-12-01T00:00:00",
"pubType": "proceedings",
"pages": "1351-1361",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-2688-6",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "268800b341",
"articleId": "1zWEnJaA69q",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "268800b362",
"articleId": "1zWEhxN28YU",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200p5974",
"title": "Deep Hough Voting for Robust Global Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5974/1BmFKwLxovS",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200g108",
"title": "Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g108/1BmGp36SSvm",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09729524",
"title": "Unsupervised Category-Specific Partial Point Set Registration via Joint Shape Completion and Registration",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09729524/1Bya8dlokw0",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/insai/2021/0859/0/085900a136",
"title": "Registration of Point Clouds: A Survey",
"doi": null,
"abstractUrl": "/proceedings-article/insai/2021/085900a136/1CHwMbhCNQA",
"parentPublication": {
"id": "proceedings/insai/2021/0859/0",
"title": "2021 International Conference on Networking Systems of AI (INSAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09858925",
"title": "Cross-Attention-Based Feature Extraction Network for 3D Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09858925/1G9E5a08Kze",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/05/09878213",
"title": "Robust Point Cloud Registration Framework Based on Deep Graph Matching",
"doi": null,
"abstractUrl": "/journal/tp/2023/05/09878213/1GrP5OekHDy",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2023/01/09942317",
"title": "Point Cloud Registration Using Multiattention Mechanism and Deep Hybrid Features",
"doi": null,
"abstractUrl": "/magazine/ex/2023/01/09942317/1I8NW3dfSG4",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10044259",
"title": "RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10044259/1KL6SJ4jOzS",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300h156",
"title": "PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300h156/1gyrIF6WuYg",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300i642",
"title": "DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300i642/1gyrzi3crw4",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1jIxhEnA8IE",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"acronym": "vrw",
"groupId": "1836626",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1jIxyayiDp6",
"doi": "10.1109/VRW50115.2020.00061",
"title": "On the Effect of Standing and Seated Viewing of 360° Videos on Subjective Quality Assessment",
"normalizedTitle": "On the Effect of Standing and Seated Viewing of 360° Videos on Subjective Quality Assessment",
"abstract": "In this paper, we compare the impact that standing and seated viewing of 360° videos on head-mounted displays has on subjective quality assessment. The statistical analysis of the data gathered in a pilot study is reported in terms of average rating times, mean opinion scores, and simulator sickness scores. The results indicate: (1) Average rating times consumed for 360° video quality assessment are similar for standing and seated viewing, (2) Higher resolving power among different quality levels is obtained for seated viewing, (3) Simulator sickness is kept significantly lower when seated.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we compare the impact that standing and seated viewing of 360° videos on head-mounted displays has on subjective quality assessment. The statistical analysis of the data gathered in a pilot study is reported in terms of average rating times, mean opinion scores, and simulator sickness scores. The results indicate: (1) Average rating times consumed for 360° video quality assessment are similar for standing and seated viewing, (2) Higher resolving power among different quality levels is obtained for seated viewing, (3) Simulator sickness is kept significantly lower when seated.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we compare the impact that standing and seated viewing of 360° videos on head-mounted displays has on subjective quality assessment. The statistical analysis of the data gathered in a pilot study is reported in terms of average rating times, mean opinion scores, and simulator sickness scores. The results indicate: (1) Average rating times consumed for 360° video quality assessment are similar for standing and seated viewing, (2) Higher resolving power among different quality levels is obtained for seated viewing, (3) Simulator sickness is kept significantly lower when seated.",
"fno": "09090456",
"keywords": [
"Data Analysis",
"Helmet Mounted Displays",
"Human Factors",
"Statistical Analysis",
"Virtual Reality",
"Head Mounted Displays",
"Simulator Sickness",
"360 X 00 B 0 Video Quality Assessment",
"Seated Viewing",
"Standing Viewing",
"Virtual Reality",
"Statistical Data Analysis",
"Videos",
"Quality Assessment",
"Virtual Reality",
"Head Mounted Displays",
"Software",
"Quality Of Experience",
"Human Centered Computing",
"Computing Methodologies",
"Virtual Reality",
"Perception"
],
"authors": [
{
"affiliation": "Blekinge Institute of Technology,371 79 Karlskrona,Sweden",
"fullName": "Yan Hu",
"givenName": "Yan",
"surname": "Hu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Blekinge Institute of Technology,371 79 Karlskrona,Sweden",
"fullName": "Majed Elwardy",
"givenName": "Majed",
"surname": "Elwardy",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Blekinge Institute of Technology,371 79 Karlskrona,Sweden",
"fullName": "Hans-Jürgen Zepernick",
"givenName": "Hans-Jürgen",
"surname": "Zepernick",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vrw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-03-01T00:00:00",
"pubType": "proceedings",
"pages": "285-286",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-6532-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09090560",
"articleId": "1jIxzjmEoeY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09090577",
"articleId": "1jIxp3AAdhK",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icmew/2018/4195/0/08551577",
"title": "Viewport-Driven Rate-Distortion Optimized Scalable Live 360° Video Network Multicast",
"doi": null,
"abstractUrl": "/proceedings-article/icmew/2018/08551577/17D45WZZ7Db",
"parentPublication": {
"id": "proceedings/icmew/2018/4195/0",
"title": "2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2019/05/08661657",
"title": "Motion parallax for 360° RGBD video",
"doi": null,
"abstractUrl": "/journal/tg/2019/05/08661657/18bmQqdj3Nu",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09736631",
"title": "On Rotation Gains Within and Beyond Perceptual Limitations for Seated VR",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09736631/1BN1UtLinTi",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2022/8402/0/840200a189",
"title": "SSV360: A Dataset on Subjetive Quality Assessment of 360° Videos for Standing and Seated Viewing on an HMD",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2022/840200a189/1CJdUQ7GJ4Q",
"parentPublication": {
"id": "proceedings/vrw/2022/8402/0",
"title": "2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2020/6532/0/09090490",
"title": "Evaluation of Simulator Sickness for 360° Videos on an HMD Subject to Participants’ Experience with Virtual Reality",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2020/09090490/1jIxwgIdgsw",
"parentPublication": {
"id": "proceedings/vrw/2020/6532/0",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ucc/2020/2394/0/239400a414",
"title": "Accuracy Analysis on 360° Virtual Reality Video Quality Assessment Methods",
"doi": null,
"abstractUrl": "/proceedings-article/ucc/2020/239400a414/1pZ0Z6h4ERq",
"parentPublication": {
"id": "proceedings/ucc/2020/2394/0",
"title": "2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2021/4057/0/405700a071",
"title": "On Head Movements in Repeated 360° Video Quality Assessment for Standing and Seated Viewing on Head Mounted Displays",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2021/405700a071/1tnXBnBVgqc",
"parentPublication": {
"id": "proceedings/vrw/2021/4057/0",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2021/1838/0/255600a198",
"title": "Assessment of the Simulator Sickness Questionnaire for Omnidirectional Videos",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2021/255600a198/1tuB40QFm92",
"parentPublication": {
"id": "proceedings/vr/2021/1838/0",
"title": "2021 IEEE Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvrv/2020/0497/0/049700a042",
"title": "Rating Duration Analysis for Subjective Quality Assessment of 360° Videos",
"doi": null,
"abstractUrl": "/proceedings-article/icvrv/2020/049700a042/1vg7TpMdSH6",
"parentPublication": {
"id": "proceedings/icvrv/2020/0497/0",
"title": "2020 International Conference on Virtual Reality and Visualization (ICVRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2021/0158/0/015800a176",
"title": "Now I’m Not Afraid: Reducing Fear of Missing Out in 360° Videos on a Head-Mounted Display using a Panoramic Thumbnail",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2021/015800a176/1yeCYYdBmPC",
"parentPublication": {
"id": "proceedings/ismar/2021/0158/0",
"title": "2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1tnWwqMuCzu",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"acronym": "vrw",
"groupId": "1836626",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1tnXBnBVgqc",
"doi": "10.1109/VRW52623.2021.00020",
"title": "On Head Movements in Repeated 360° Video Quality Assessment for Standing and Seated Viewing on Head Mounted Displays",
"normalizedTitle": "On Head Movements in Repeated 360° Video Quality Assessment for Standing and Seated Viewing on Head Mounted Displays",
"abstract": "Watching 360° videos on head mounted displays (HMDs) allows viewers to explore scenes in all directions. In this paper, we focus on investigating the head movements of two participants for standing and seated viewing of a total of 720 360° videos on HMDs. The head movements were recorded in a 360° video quality assessment experiment which was repeated after a long and short break between sessions to study changes in viewing behavior over time. The analysis of the head movement data is provided as histograms of head rotations, head speed, head turns, and head trajectories. It is shown that the participants have their own distinct exploration behavior for standing viewing which becomes less different for seated viewing.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Watching 360° videos on head mounted displays (HMDs) allows viewers to explore scenes in all directions. In this paper, we focus on investigating the head movements of two participants for standing and seated viewing of a total of 720 360° videos on HMDs. The head movements were recorded in a 360° video quality assessment experiment which was repeated after a long and short break between sessions to study changes in viewing behavior over time. The analysis of the head movement data is provided as histograms of head rotations, head speed, head turns, and head trajectories. It is shown that the participants have their own distinct exploration behavior for standing viewing which becomes less different for seated viewing.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Watching 360° videos on head mounted displays (HMDs) allows viewers to explore scenes in all directions. In this paper, we focus on investigating the head movements of two participants for standing and seated viewing of a total of 720 360° videos on HMDs. The head movements were recorded in a 360° video quality assessment experiment which was repeated after a long and short break between sessions to study changes in viewing behavior over time. The analysis of the head movement data is provided as histograms of head rotations, head speed, head turns, and head trajectories. It is shown that the participants have their own distinct exploration behavior for standing viewing which becomes less different for seated viewing.",
"fno": "405700a071",
"keywords": [
"Helmet Mounted Displays",
"Video Signal Processing",
"Seated Viewing",
"Head Mounted Displays",
"HM Ds",
"Head Movement Data",
"Head Rotations",
"Head Speed",
"Head Turns",
"Head Trajectories",
"Standing Viewing",
"360 Video Quality Assessment",
"Histograms",
"Three Dimensional Displays",
"Conferences",
"Virtual Reality",
"User Interfaces",
"Magnetic Heads",
"Quality Assessment",
"Human Centered Computing",
"Computing Methodologies",
"Virtual Reality",
"Head Movements"
],
"authors": [
{
"affiliation": "Blekinge Institute of Technology,Karlskrona,Sweden,371 79",
"fullName": "Majed Elwardy",
"givenName": "Majed",
"surname": "Elwardy",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Blekinge Institute of Technology,Karlskrona,Sweden,371 79",
"fullName": "Hans-Jürgen Zepernick",
"givenName": "Hans-Jürgen",
"surname": "Zepernick",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Blekinge Institute of Technology,Karlskrona,Sweden,371 79",
"fullName": "Yan Hu",
"givenName": "Yan",
"surname": "Hu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vrw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-03-01T00:00:00",
"pubType": "proceedings",
"pages": "71-74",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-4057-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "405700a067",
"articleId": "1tnWChd88wM",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "405700a075",
"articleId": "1tnWML9rdVC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/vr/2017/6647/0/07892227",
"title": "Guided head rotation and amplified head rotation: Evaluating semi-natural travel and viewing techniques in virtual reality",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2017/07892227/12OmNwseEYz",
"parentPublication": {
"id": "proceedings/vr/2017/6647/0",
"title": "2017 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2018/04/08260916",
"title": "Parallax360: Stereoscopic 360° Scene Representation for Head-Motion Parallax",
"doi": null,
"abstractUrl": "/journal/tg/2018/04/08260916/13rRUyp7tX1",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2019/05/08661657",
"title": "Motion parallax for 360° RGBD video",
"doi": null,
"abstractUrl": "/journal/tg/2019/05/08661657/18bmQqdj3Nu",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2022/9617/0/961700a001",
"title": "Bullet Comments for 360°Video",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2022/961700a001/1CJcgerbwNa",
"parentPublication": {
"id": "proceedings/vr/2022/9617/0",
"title": "2022 IEEE on Conference Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2022/8402/0/840200a189",
"title": "SSV360: A Dataset on Subjetive Quality Assessment of 360° Videos for Standing and Seated Viewing on an HMD",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2022/840200a189/1CJdUQ7GJ4Q",
"parentPublication": {
"id": "proceedings/vrw/2022/8402/0",
"title": "2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/mu/2022/02/09779506",
"title": "Why VR Games Sickness? An Empirical Study of Capturing and Analyzing VR Games Head Movement Dataset",
"doi": null,
"abstractUrl": "/magazine/mu/2022/02/09779506/1DwUBBXPkVG",
"parentPublication": {
"id": "mags/mu",
"title": "IEEE MultiMedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2020/6532/0/09090456",
"title": "On the Effect of Standing and Seated Viewing of 360° Videos on Subjective Quality Assessment",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2020/09090456/1jIxyayiDp6",
"parentPublication": {
"id": "proceedings/vrw/2020/6532/0",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ism/2020/8697/0/869700a082",
"title": "Redefine the A in ABR for 360-degree Videos: A Flexible ABR Framework",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2020/869700a082/1qBbIEON8UU",
"parentPublication": {
"id": "proceedings/ism/2020/8697/0",
"title": "2020 IEEE International Symposium on Multimedia (ISM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvrv/2020/0497/0/049700a042",
"title": "Rating Duration Analysis for Subjective Quality Assessment of 360° Videos",
"doi": null,
"abstractUrl": "/proceedings-article/icvrv/2020/049700a042/1vg7TpMdSH6",
"parentPublication": {
"id": "proceedings/icvrv/2020/0497/0",
"title": "2020 International Conference on Virtual Reality and Visualization (ICVRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2021/0158/0/015800a176",
"title": "Now I’m Not Afraid: Reducing Fear of Missing Out in 360° Videos on a Head-Mounted Display using a Panoramic Thumbnail",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2021/015800a176/1yeCYYdBmPC",
"parentPublication": {
"id": "proceedings/ismar/2021/0158/0",
"title": "2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1tnWwqMuCzu",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"acronym": "vrw",
"groupId": "1836626",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1tnXe22MFJm",
"doi": "10.1109/VRW52623.2021.00071",
"title": "Revisiting Audiovisual Rotation Gains for Redirected Walking",
"normalizedTitle": "Revisiting Audiovisual Rotation Gains for Redirected Walking",
"abstract": "In this paper, we present a psychophysical study exploring how spatialized sound affects perceptual detection thresholds for rotation gains during exposure to virtual environments with varying degrees of visibility. The study was based on a 2×3 factorial design, crossing two types of audio (no audio and spatialized audio) and three degrees of visibility (low, medium, and high density fog). We found no notable effects of sound spatialization or visibility on detection thresholds. Although future studies are required to empirically confirm that vision dominates audition, these results provide quantitative evidence that visual rotation gains may be robust to auditory interference. Furthermore, they suggest that rotation gains may be useful even when the virtual environment offers very limited visibility.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we present a psychophysical study exploring how spatialized sound affects perceptual detection thresholds for rotation gains during exposure to virtual environments with varying degrees of visibility. The study was based on a 2×3 factorial design, crossing two types of audio (no audio and spatialized audio) and three degrees of visibility (low, medium, and high density fog). We found no notable effects of sound spatialization or visibility on detection thresholds. Although future studies are required to empirically confirm that vision dominates audition, these results provide quantitative evidence that visual rotation gains may be robust to auditory interference. Furthermore, they suggest that rotation gains may be useful even when the virtual environment offers very limited visibility.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we present a psychophysical study exploring how spatialized sound affects perceptual detection thresholds for rotation gains during exposure to virtual environments with varying degrees of visibility. The study was based on a 2×3 factorial design, crossing two types of audio (no audio and spatialized audio) and three degrees of visibility (low, medium, and high density fog). We found no notable effects of sound spatialization or visibility on detection thresholds. Although future studies are required to empirically confirm that vision dominates audition, these results provide quantitative evidence that visual rotation gains may be robust to auditory interference. Furthermore, they suggest that rotation gains may be useful even when the virtual environment offers very limited visibility.",
"fno": "405700a358",
"keywords": [
"Hearing",
"Virtual Reality",
"High Density Fog",
"Notable Effects",
"Sound Spatialization",
"Visual Rotation Gains",
"Virtual Environment",
"Audiovisual Rotation Gains",
"Redirected Walking",
"Psychophysical Study",
"Spatialized Sound",
"Perceptual Detection Thresholds",
"2 X 00 D 7 3 Factorial Design",
"Spatialized Audio",
"Low Density Fog",
"Legged Locomotion",
"Visualization",
"Three Dimensional Displays",
"Conferences",
"Virtual Environments",
"Interference",
"User Interfaces",
"Human Centered Computing",
"Human Computer Interaction HCI",
"Interaction Paradigms",
"Virtual Reality"
],
"authors": [
{
"affiliation": "Aalborg University",
"fullName": "Andreas Junker",
"givenName": "Andreas",
"surname": "Junker",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Aalborg University",
"fullName": "Carl Hutters",
"givenName": "Carl",
"surname": "Hutters",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Aalborg University",
"fullName": "Daniel Reipur",
"givenName": "Daniel",
"surname": "Reipur",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Aalborg University",
"fullName": "Lasse Embøl",
"givenName": "Lasse",
"surname": "Embøl",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Aalborg University",
"fullName": "Niels Christian Nilsson",
"givenName": "Niels Christian",
"surname": "Nilsson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Aalborg University",
"fullName": "Stefania Serafin",
"givenName": "Stefania",
"surname": "Serafin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Minnesota",
"fullName": "Evan Suma Rosenberg",
"givenName": "Evan Suma",
"surname": "Rosenberg",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "vrw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-03-01T00:00:00",
"pubType": "proceedings",
"pages": "358-359",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-4057-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "405700a353",
"articleId": "1tnXN3fYARG",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "405700a360",
"articleId": "1tnXyozrEgE",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/vr/2017/6647/0/07892279",
"title": "Curvature gains in redirected walking: A closer look",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2017/07892279/12OmNBEGYJE",
"parentPublication": {
"id": "proceedings/vr/2017/6647/0",
"title": "2017 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2014/2871/0/06802053",
"title": "An enhanced steering algorithm for redirected walking in virtual environments",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2014/06802053/12OmNCbU2Wt",
"parentPublication": {
"id": "proceedings/vr/2014/2871/0",
"title": "2014 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2016/0836/0/07504743",
"title": "Estimation of detection thresholds for audiovisual rotation gains",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2016/07504743/12OmNzmcm0b",
"parentPublication": {
"id": "proceedings/vr/2016/0836/0",
"title": "2016 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2018/3365/0/08446479",
"title": "Adopting the Roll Manipulation for Redirected Walking",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446479/13bd1eSlys4",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2018/3365/0/08446062",
"title": "Biomechanical Parameters Under Curvature Gains and Bending Gains in Redirected Walking",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446062/13bd1fKQxrR",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2018/3365/0/08446225",
"title": "Effect of Environment Size on Curvature Redirected Walking Thresholds",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446225/13bd1sx4Zt8",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2018/04/08260943",
"title": "You Spin my Head Right Round: Threshold of Limited Immersion for Rotation Gains in Redirected Walking",
"doi": null,
"abstractUrl": "/journal/tg/2018/04/08260943/13rRUNvyato",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2018/7459/0/745900a115",
"title": "Rethinking Redirected Walking: On the Use of Curvature Gains Beyond Perceptual Limitations and Revisiting Bending Gains",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2018/745900a115/17D45WK5AlG",
"parentPublication": {
"id": "proceedings/ismar/2018/7459/0",
"title": "2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2022/8402/0/840200a830",
"title": "Redirected Walking in 360° Video: Effect of Environment Size on Detection Thresholds for Translation and Rotation Gains",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2022/840200a830/1CJd1TReEYo",
"parentPublication": {
"id": "proceedings/vrw/2022/8402/0",
"title": "2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2019/1377/0/08798117",
"title": "Estimation of Rotation Gain Thresholds for Redirected Walking Considering FOV and Gender",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2019/08798117/1cJ1fo5PwqY",
"parentPublication": {
"id": "proceedings/vr/2019/1377/0",
"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNykCcdi",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"acronym": "cvprw",
"groupId": "1001809",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNyYm2D8",
"doi": "10.1109/CVPRW.2016.171",
"title": "3D Convolutional Networks-Based Mitotic Event Detection in Time-Lapse Phase Contrast Microscopy Image Sequences of Stem Cell Populations",
"normalizedTitle": "3D Convolutional Networks-Based Mitotic Event Detection in Time-Lapse Phase Contrast Microscopy Image Sequences of Stem Cell Populations",
"abstract": "In this paper, we propose a straightforward and effective method for mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. Different from most of recent methods leveraging temporal modeling to learn the latent dynamics within one mitotic event, we mainly target on the data-driven spatio-temporal visual feature learning for mitotic event representation to bypass the difficulties in both robust hand-crafted feature designing and complicated temporal dynamic learning. Specially, we design the architecture of the convolutional neural networks with 3D filters to extract the holistic feature of the volumetric region where individual mitosis event occurs. Then, the extracted features can be directly feeded into the off-the-shelf classifiers for model learning or inference. Moreover, we prepare a novel and challenging dataset for mitosis detection. The comparison experiments demonstrate the superiority of the proposed method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we propose a straightforward and effective method for mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. Different from most of recent methods leveraging temporal modeling to learn the latent dynamics within one mitotic event, we mainly target on the data-driven spatio-temporal visual feature learning for mitotic event representation to bypass the difficulties in both robust hand-crafted feature designing and complicated temporal dynamic learning. Specially, we design the architecture of the convolutional neural networks with 3D filters to extract the holistic feature of the volumetric region where individual mitosis event occurs. Then, the extracted features can be directly feeded into the off-the-shelf classifiers for model learning or inference. Moreover, we prepare a novel and challenging dataset for mitosis detection. The comparison experiments demonstrate the superiority of the proposed method.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we propose a straightforward and effective method for mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. Different from most of recent methods leveraging temporal modeling to learn the latent dynamics within one mitotic event, we mainly target on the data-driven spatio-temporal visual feature learning for mitotic event representation to bypass the difficulties in both robust hand-crafted feature designing and complicated temporal dynamic learning. Specially, we design the architecture of the convolutional neural networks with 3D filters to extract the holistic feature of the volumetric region where individual mitosis event occurs. Then, the extracted features can be directly feeded into the off-the-shelf classifiers for model learning or inference. Moreover, we prepare a novel and challenging dataset for mitosis detection. The comparison experiments demonstrate the superiority of the proposed method.",
"fno": "1437b359",
"keywords": [
"Three Dimensional Displays",
"Feature Extraction",
"Hidden Markov Models",
"Kernel",
"Convolution",
"Image Sequences",
"Microscopy"
],
"authors": [
{
"affiliation": null,
"fullName": "Wei-Zhi Nie",
"givenName": "Wei-Zhi",
"surname": "Nie",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Wen-Hui Li",
"givenName": "Wen-Hui",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "An-An Liu",
"givenName": "An-An",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Tong Hao",
"givenName": "Tong",
"surname": "Hao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yu-Ting Su",
"givenName": "Yu-Ting",
"surname": "Su",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvprw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-06-01T00:00:00",
"pubType": "proceedings",
"pages": "1359-1366",
"year": "2016",
"issn": "2160-7516",
"isbn": "978-1-5090-1437-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "1437b350",
"articleId": "12OmNBigFxU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "1437b367",
"articleId": "12OmNAkWvfH",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2012/2216/0/06460626",
"title": "Learning-based mitotic cell detection in histopathological images",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460626/12OmNApu5JD",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2006/2597/1/01640770",
"title": "Feature Selection for Evaluating Fluorescence Microscopy Images in Genome-Wide Cell Screens",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2006/01640770/12OmNBDgZ0q",
"parentPublication": {
"id": "proceedings/cvpr/2006/2597/2",
"title": "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2011/0394/0/05995717",
"title": "Detection of mitosis within a stem cell population of high cell confluence in phase-contrast microscopy images",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2011/05995717/12OmNyKrHdl",
"parentPublication": {
"id": "proceedings/cvpr/2011/0394/0",
"title": "CVPR 2011",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2010/7491/0/05583299",
"title": "Spatiotemporal mitosis event detection in time-lapse phase contrast microscopy image sequences",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2010/05583299/12OmNyUWR2g",
"parentPublication": {
"id": "proceedings/icme/2010/7491/0",
"title": "2010 IEEE International Conference on Multimedia and Expo",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2017/04/07962189",
"title": "Mitosis Detection in Phase Contrast Microscopy Image Sequences of Stem Cell Populations: A Critical Review",
"doi": null,
"abstractUrl": "/journal/bd/2017/04/07962189/13rRUxBa5dP",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2017/04/07984862",
"title": "Modeling Temporal Information of Mitotic for Mitotic Event Detection",
"doi": null,
"abstractUrl": "/journal/bd/2017/04/07984862/13rRUypp59L",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2022/02/09177268",
"title": "Deep Reinforcement Learning-Based Progressive Sequence Saliency Discovery Network for Mitosis Detection In Time-Lapse Phase-Contrast Microscopy Images",
"doi": null,
"abstractUrl": "/journal/tb/2022/02/09177268/1mA5zm0aQVO",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313500",
"title": "Sequence-level Supervised Deep Neural Networks for Mitosis Event Detection in Time-Lapse Microscopy Images",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313500/1qmfHSDHqkE",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2021/02/08723196",
"title": "A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy Image",
"doi": null,
"abstractUrl": "/journal/tb/2021/02/08723196/1sA4RbsJNcs",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2020/0445/0/044500a100",
"title": "A Deep Framework for Cell Mitosis Detection in Microscopy Images",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2020/044500a100/1t90oOgvrby",
"parentPublication": {
"id": "proceedings/cis/2020/0445/0",
"title": "2020 16th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1BmEezmpGrm",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"acronym": "iccv",
"groupId": "1000149",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1BmFzgP28iA",
"doi": "10.1109/ICCV48922.2021.00492",
"title": "Adaptive Graph Convolution for Point Cloud Analysis",
"normalizedTitle": "Adaptive Graph Convolution for Point Cloud Analysis",
"abstract": "Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect. The standard convolution characterises feature correspondences indistinguishably among 3D points, presenting an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike popular attentional weight schemes, the proposed AdaptConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive qualitative and quantitative evaluations show that our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets. Our code is available at https://github.com/hrzhou2/AdaptConv-master.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect. The standard convolution characterises feature correspondences indistinguishably among 3D points, presenting an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike popular attentional weight schemes, the proposed AdaptConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive qualitative and quantitative evaluations show that our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets. Our code is available at https://github.com/hrzhou2/AdaptConv-master.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect. The standard convolution characterises feature correspondences indistinguishably among 3D points, presenting an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike popular attentional weight schemes, the proposed AdaptConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive qualitative and quantitative evaluations show that our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets. Our code is available at https://github.com/hrzhou2/AdaptConv-master.",
"fno": "281200e945",
"keywords": [
"Point Cloud Compression",
"Representation Learning",
"Computer Vision",
"Three Dimensional Displays",
"Convolution",
"Shape",
"Semantics",
"Vision Applications And Systems",
"3 D From A Single Image And Shape From X",
"Stereo",
"3 D From Multiview And Other Sensors"
],
"authors": [
{
"affiliation": "Nanjing University",
"fullName": "Haoran Zhou",
"givenName": "Haoran",
"surname": "Zhou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University of Aeronautics and Astronautics",
"fullName": "Yidan Feng",
"givenName": "Yidan",
"surname": "Feng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University",
"fullName": "Mingsheng Fang",
"givenName": "Mingsheng",
"surname": "Fang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University of Aeronautics and Astronautics",
"fullName": "Mingqiang Wei",
"givenName": "Mingqiang",
"surname": "Wei",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The Hong Kong Polytechnic University",
"fullName": "Jing Qin",
"givenName": "Jing",
"surname": "Qin",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Nanjing University",
"fullName": "Tong Lu",
"givenName": "Tong",
"surname": "Lu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-10-01T00:00:00",
"pubType": "proceedings",
"pages": "4945-4954",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-2812-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "281200e935",
"articleId": "1BmHjltqCUU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "281200e955",
"articleId": "1BmHH09mUx2",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200h417",
"title": "Differentiable Convolution Search for Point Cloud Processing",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h417/1BmFWhjjw8o",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200k0448",
"title": "SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200k0448/1BmI7U4COL6",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmew/2022/7218/0/09859382",
"title": "Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icmew/2022/09859382/1G4F1tWFNbq",
"parentPublication": {
"id": "proceedings/icmew/2022/7218/0",
"title": "2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/05/09928362",
"title": "Dynamic Convolution for 3D Point Cloud Instance Segmentation",
"doi": null,
"abstractUrl": "/journal/tp/2023/05/09928362/1HJusP2EGEE",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956365",
"title": "Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956365/1IHp3Rccu2c",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10024001",
"title": "AGConv: Adaptive Graph Convolution on 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10024001/1K9spf0w0Ug",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300k0288",
"title": "Graph Attention Convolution for Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300k0288/1gyrHUpmp68",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300i887",
"title": "Relation-Shape Convolutional Neural Network for Point Cloud Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300i887/1gyscVWoWqc",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/08/09355025",
"title": "Learning of 3D Graph Convolution Networks for Point Cloud Analysis",
"doi": null,
"abstractUrl": "/journal/tp/2022/08/09355025/1rgCbgC4Z8s",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2021/0898/0/089800b480",
"title": "Geometric Invariant Representation Learning for 3D Point Cloud",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2021/089800b480/1zw6hJOIDcs",
"parentPublication": {
"id": "proceedings/ictai/2021/0898/0",
"title": "2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1gyr6w5YIIU",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"acronym": "cvpr",
"groupId": "1000147",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1gyrHUpmp68",
"doi": "10.1109/CVPR.2019.01054",
"title": "Graph Attention Convolution for Point Cloud Semantic Segmentation",
"normalizedTitle": "Graph Attention Convolution for Point Cloud Semantic Segmentation",
"abstract": "Standard convolution is inherently limited for semantic segmentation of point cloud due to its isotropy about features. It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result. This paper proposes a novel graph attention convolution (GAC), whose kernels can be dynamically carved into specific shapes to adapt to the structure of an object. Specifically, by assigning proper attentional weights to different neighboring points, GAC is designed to selectively focus on the most relevant part of them according to their dynamically learned features. The shape of the convolution kernel is then determined by the learned distribution of the attentional weights. Though simple, GAC can capture the structured features of point clouds for fine-grained segmentation and avoid feature contamination between objects. Theoretically, we provided a thorough analysis on the expressive capabilities of GAC to show how it can learn about the features of point clouds. Empirically, we evaluated the proposed GAC on challenging indoor and outdoor datasets and achieved the state-of-the-art results in both scenarios.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Standard convolution is inherently limited for semantic segmentation of point cloud due to its isotropy about features. It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result. This paper proposes a novel graph attention convolution (GAC), whose kernels can be dynamically carved into specific shapes to adapt to the structure of an object. Specifically, by assigning proper attentional weights to different neighboring points, GAC is designed to selectively focus on the most relevant part of them according to their dynamically learned features. The shape of the convolution kernel is then determined by the learned distribution of the attentional weights. Though simple, GAC can capture the structured features of point clouds for fine-grained segmentation and avoid feature contamination between objects. Theoretically, we provided a thorough analysis on the expressive capabilities of GAC to show how it can learn about the features of point clouds. Empirically, we evaluated the proposed GAC on challenging indoor and outdoor datasets and achieved the state-of-the-art results in both scenarios.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Standard convolution is inherently limited for semantic segmentation of point cloud due to its isotropy about features. It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result. This paper proposes a novel graph attention convolution (GAC), whose kernels can be dynamically carved into specific shapes to adapt to the structure of an object. Specifically, by assigning proper attentional weights to different neighboring points, GAC is designed to selectively focus on the most relevant part of them according to their dynamically learned features. The shape of the convolution kernel is then determined by the learned distribution of the attentional weights. Though simple, GAC can capture the structured features of point clouds for fine-grained segmentation and avoid feature contamination between objects. Theoretically, we provided a thorough analysis on the expressive capabilities of GAC to show how it can learn about the features of point clouds. Empirically, we evaluated the proposed GAC on challenging indoor and outdoor datasets and achieved the state-of-the-art results in both scenarios.",
"fno": "329300k0288",
"keywords": [
"Convolutional Neural Nets",
"Graph Theory",
"Image Segmentation",
"Learning Artificial Intelligence",
"GAC",
"Attentional Weights",
"Neighboring Points",
"Convolution Kernel",
"Fine Grained Segmentation",
"Point Cloud Semantic Segmentation",
"Graph Attention Convolution",
"Point Cloud Compression",
"Representation Learning",
"Three Dimensional Displays",
"Convolution",
"Shape",
"Semantics",
"Pattern Recognition",
"Scene Analysis And Understanding",
"3 D From Multiview And Sensors",
"Deep Learning",
"Segmentation",
"Grouping And Shape"
],
"authors": [
{
"affiliation": "Wuhan Univ.",
"fullName": "Lei Wang",
"givenName": "Lei",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Wuhan Univ.",
"fullName": "Yuchun Huang",
"givenName": "Yuchun",
"surname": "Huang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Wuhan Univ.",
"fullName": "Yaolin Hou",
"givenName": "Yaolin",
"surname": "Hou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Wuhan Univ.",
"fullName": "Shenman Zhang",
"givenName": "Shenman",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Purdue",
"fullName": "Jie Shan",
"givenName": "Jie",
"surname": "Shan",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvpr",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-06-01T00:00:00",
"pubType": "proceedings",
"pages": "10288-10297",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-3293-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "329300k0277",
"articleId": "1gyrYacs8nK",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "329300k0298",
"articleId": "1gyrfMEmMlq",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200e945",
"title": "Adaptive Graph Convolution for Point Cloud Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200e945/1BmFzgP28iA",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200h078",
"title": "Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h078/1BmH5me34BO",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200h098",
"title": "TempNet: Online Semantic Segmentation on Large-scale Point Cloud Series",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h098/1BmJCgdfMAM",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859693",
"title": "Cenet: Toward Concise and Efficient Lidar Semantic Segmentation for Autonomous Driving",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859693/1G9Ek0QiHvy",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956365",
"title": "Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956365/1IHp3Rccu2c",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10024001",
"title": "AGConv: Adaptive Graph Convolution on 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10024001/1K9spf0w0Ug",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800n3062",
"title": "Squeeze-and-Attention Networks for Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800n3062/1m3ns2rI5GM",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aiea/2020/8288/0/828800a354",
"title": "Road Segmentation Using Point Cloud BEV Based on Fully Convolution Network",
"doi": null,
"abstractUrl": "/proceedings-article/aiea/2020/828800a354/1nTujLhJKnK",
"parentPublication": {
"id": "proceedings/aiea/2020/8288/0",
"title": "2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412034",
"title": "Boundary-aware Graph Convolution for Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412034/1tmijSLCoZq",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2021/0898/0/089800b480",
"title": "Geometric Invariant Representation Learning for 3D Point Cloud",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2021/089800b480/1zw6hJOIDcs",
"parentPublication": {
"id": "proceedings/ictai/2021/0898/0",
"title": "2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1hQqfuoOyHu",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"acronym": "iccv",
"groupId": "1000149",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1hQqibv9Neg",
"doi": "10.1109/ICCV.2019.00827",
"title": "Geometric Disentanglement for Generative Latent Shape Models",
"normalizedTitle": "Geometric Disentanglement for Generative Latent Shape Models",
"abstract": "Representing 3D shapes is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a generative model of shapes that is interpretable and easily manipulated, particularly in the absence of supervised labels. In this paper, we propose an unsupervised approach to partitioning the latent space of a variational autoencoder for 3D point clouds in a natural way, using only geometric information, that builds upon prior work utilizing generative adversarial models of point sets. Our method makes use of tools from spectral geometry to separate intrinsic and extrinsic shape information, and then considers several hierarchical disentanglement penalties for dividing the latent space in this manner. We also propose a novel disentanglement penalty that penalizes the predicted change in the latent representation of the output,with respect to the latent variables of the initial shape. We show that the resulting latent representation exhibits intuitive and interpretable behaviour, enabling tasks such as pose transfer that cannot easily be performed by models with an entangled representation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Representing 3D shapes is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a generative model of shapes that is interpretable and easily manipulated, particularly in the absence of supervised labels. In this paper, we propose an unsupervised approach to partitioning the latent space of a variational autoencoder for 3D point clouds in a natural way, using only geometric information, that builds upon prior work utilizing generative adversarial models of point sets. Our method makes use of tools from spectral geometry to separate intrinsic and extrinsic shape information, and then considers several hierarchical disentanglement penalties for dividing the latent space in this manner. We also propose a novel disentanglement penalty that penalizes the predicted change in the latent representation of the output,with respect to the latent variables of the initial shape. We show that the resulting latent representation exhibits intuitive and interpretable behaviour, enabling tasks such as pose transfer that cannot easily be performed by models with an entangled representation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Representing 3D shapes is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a generative model of shapes that is interpretable and easily manipulated, particularly in the absence of supervised labels. In this paper, we propose an unsupervised approach to partitioning the latent space of a variational autoencoder for 3D point clouds in a natural way, using only geometric information, that builds upon prior work utilizing generative adversarial models of point sets. Our method makes use of tools from spectral geometry to separate intrinsic and extrinsic shape information, and then considers several hierarchical disentanglement penalties for dividing the latent space in this manner. We also propose a novel disentanglement penalty that penalizes the predicted change in the latent representation of the output,with respect to the latent variables of the initial shape. We show that the resulting latent representation exhibits intuitive and interpretable behaviour, enabling tasks such as pose transfer that cannot easily be performed by models with an entangled representation.",
"fno": "480300i180",
"keywords": [
"Computational Geometry",
"Computer Vision",
"Image Representation",
"Neural Nets",
"Shape Recognition",
"Solid Modelling",
"Statistical Analysis",
"Supervised Learning",
"Unsupervised Learning",
"Latent Variables",
"Interpretable Behaviour",
"Geometric Disentanglement",
"Generative Latent Shape Models",
"3 D Shapes",
"Artificial Intelligence",
"Computer Vision",
"Open Problem",
"Supervised Labels",
"Unsupervised Approach",
"Latent Space",
"Spectral Geometry",
"Variational Autoencoder",
"Latent Representation",
"Hierarchical Disentanglement Penalties",
"Extrinsic Shape Information",
"Separate Intrinsic Shape Information",
"Generative Adversarial Models",
"Geometric Information",
"3 D Point Clouds",
"Shape",
"Three Dimensional Displays",
"Computational Modeling",
"Quaternions",
"Task Analysis",
"Gallium Nitride",
"Geometry"
],
"authors": [
{
"affiliation": "University of Toronto",
"fullName": "Tristan Aumentado-Armstrong",
"givenName": "Tristan",
"surname": "Aumentado-Armstrong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Toronto",
"fullName": "Stavros Tsogkas",
"givenName": "Stavros",
"surname": "Tsogkas",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Samsung",
"fullName": "Allan Jepson",
"givenName": "Allan",
"surname": "Jepson",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Toronto",
"fullName": "Sven Dickinson",
"givenName": "Sven",
"surname": "Dickinson",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iccv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-10-01T00:00:00",
"pubType": "proceedings",
"pages": "8180-8189",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-4803-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "480300i171",
"articleId": "1hQqykdpFq8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "480300i190",
"articleId": "1hQqwnCJniM",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2018/6420/0/642000a878",
"title": "GAGAN: Geometry-Aware Generative Adversarial Networks",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000a878/17D45W2WyzD",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000g606",
"title": "Generative Adversarial Image Synthesis with Decision Tree Latent Controller",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000g606/17D45WYQJ62",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2019/1975/0/197500a848",
"title": "Style and Content Disentanglement in Generative Adversarial Networks",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2019/197500a848/18j8FazPuwM",
"parentPublication": {
"id": "proceedings/wacv/2019/1975/0",
"title": "2019 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600s8709",
"title": "3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600s8709/1H0LamYZIhq",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600s8531",
"title": "GLASS: Geometric Latent Augmentation for Shape Spaces",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600s8531/1H1lnfXXaHS",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093375",
"title": "Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093375/1jPbnaeKnlu",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800j240",
"title": "Interpreting the Latent Space of GANs for Semantic Face Editing",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800j240/1m3nRsHWYJa",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800h917",
"title": "Guided Variational Autoencoder for Disentanglement Learning",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800h917/1m3oiUnuaIM",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800o4092",
"title": "Adversarial Latent Autoencoders",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800o4092/1m3okyROwx2",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2020/8128/0/812800a868",
"title": "GIF: Generative Interpretable Faces",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2020/812800a868/1qyxnIhctWg",
"parentPublication": {
"id": "proceedings/3dv/2020/8128/0",
"title": "2020 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1uqGdWlamUo",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"acronym": "wacv",
"groupId": "1000040",
"volume": "0",
"displayVolume": "0",
"year": "2021",
"__typename": "ProceedingType"
},
"article": {
"id": "1uqGAEg8Jaw",
"doi": "10.1109/WACV48630.2021.00319",
"title": "Cross-Domain Latent Modulation for Variational Transfer Learning",
"normalizedTitle": "Cross-Domain Latent Modulation for Variational Transfer Learning",
"abstract": "We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep representations from one data domain and use it as perturbation to the reparameterization of the latent variable in another domain. Specifically, deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. Second, the learned deep representations are cross-modulated to the latent encoding of the alternate domain. The consistency between the reconstruction from the modulated latent encoding and the generation using deep representation samples is then enforced in order to produce inter-class alignment in the latent space. We apply the proposed model to a number of transfer learning tasks including unsupervised domain adaptation and image-to-image translation. Experimental results show that our model gives competitive performance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep representations from one data domain and use it as perturbation to the reparameterization of the latent variable in another domain. Specifically, deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. Second, the learned deep representations are cross-modulated to the latent encoding of the alternate domain. The consistency between the reconstruction from the modulated latent encoding and the generation using deep representation samples is then enforced in order to produce inter-class alignment in the latent space. We apply the proposed model to a number of transfer learning tasks including unsupervised domain adaptation and image-to-image translation. Experimental results show that our model gives competitive performance.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep representations from one data domain and use it as perturbation to the reparameterization of the latent variable in another domain. Specifically, deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. Second, the learned deep representations are cross-modulated to the latent encoding of the alternate domain. The consistency between the reconstruction from the modulated latent encoding and the generation using deep representation samples is then enforced in order to produce inter-class alignment in the latent space. We apply the proposed model to a number of transfer learning tasks including unsupervised domain adaptation and image-to-image translation. Experimental results show that our model gives competitive performance.",
"fno": "047700d148",
"keywords": [
"Computer Vision",
"Deep Learning Artificial Intelligence",
"Image Reconstruction",
"Image Representation",
"Variational Transfer Learning",
"Cross Domain Latent Modulation Mechanism",
"Variational Autoencoders Framework",
"Data Domain",
"Inference Model",
"Deep Representations",
"Latent Encoding",
"Unsupervised Domain Adaptation",
"Latent Variable Reparameterization",
"Image To Image Translation",
"Gradient Reversal",
"Adaptation Models",
"Visualization",
"Computer Vision",
"Perturbation Methods",
"Conferences",
"Transfer Learning",
"Modulation"
],
"authors": [
{
"affiliation": "University of Otago,Department of Information Science",
"fullName": "Jinyong Hou",
"givenName": "Jinyong",
"surname": "Hou",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Otago,Department of Information Science",
"fullName": "Jeremiah D. Deng",
"givenName": "Jeremiah D.",
"surname": "Deng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Otago,Department of Information Science",
"fullName": "Stephen Cranefield",
"givenName": "Stephen",
"surname": "Cranefield",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Otago,Department of Information Science",
"fullName": "Xuejie Ding",
"givenName": "Xuejie",
"surname": "Ding",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "wacv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2021-01-01T00:00:00",
"pubType": "proceedings",
"pages": "3148-3157",
"year": "2021",
"issn": null,
"isbn": "978-1-6654-0477-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "047700d138",
"articleId": "1uqGr1LwSeQ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "047700d158",
"articleId": "1uqGv2LZVLO",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/icdmw/2014/4274/0/4274a259",
"title": "Supervised Adaptive-Transfer PLSA for Cross-Domain Text Classification",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2014/4274a259/12OmNzb7ZoA",
"parentPublication": {
"id": "proceedings/icdmw/2014/4274/0",
"title": "2014 IEEE International Conference on Data Mining Workshop (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2018/07/07970176",
"title": "Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning",
"doi": null,
"abstractUrl": "/journal/tp/2018/07/07970176/13rRUxZ0o2R",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2018/1737/0/08486513",
"title": "TLR: Transfer Latent Representation for Unsupervised Domain Adaptation",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2018/08486513/14jQfNzRkmY",
"parentPublication": {
"id": "proceedings/icme/2018/1737/0",
"title": "2018 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000d771",
"title": "Boosting Domain Adaptation by Discovering Latent Domains",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000d771/17D45WZZ7ES",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000i837",
"title": "Deep Cross-Media Knowledge Transfer",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000i837/17D45XzbnKu",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200h738",
"title": "Reliably fast adversarial training via latent adversarial perturbation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h738/1BmGTglwcQE",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/01/09904483",
"title": "IDLat: An Importance-Driven Latent Generation Method for Scientific Data",
"doi": null,
"abstractUrl": "/journal/tg/2023/01/09904483/1H1gfbVcpgI",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2022/5099/0/509900b269",
"title": "A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2022/509900b269/1KpCqZ3W4YE",
"parentPublication": {
"id": "proceedings/icdm/2022/5099/0",
"title": "2022 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2021/02/08792192",
"title": "Inferring Latent Domains for Unsupervised Deep Domain Adaptation",
"doi": null,
"abstractUrl": "/journal/tp/2021/02/08792192/1ckpnS5WCqs",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09412896",
"title": "Variational Inference with Latent Space Quantization for Adversarial Resilience",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09412896/1tmjDkyoo24",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNwD1pUh",
"title": "2009 42nd Hawaii International Conference on System Sciences",
"acronym": "hicss",
"groupId": "1000730",
"volume": "0",
"displayVolume": "0",
"year": "2009",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNy6Zs1C",
"doi": "10.1109/HICSS.2009.672",
"title": "Digital Cross-Organizational and Cross-Border Collaboration: A Scientometric Study",
"normalizedTitle": "Digital Cross-Organizational and Cross-Border Collaboration: A Scientometric Study",
"abstract": "Digital cross-organizational and cross-border collaboration are emerging research issues. Significant drivers of this development are collaboration-related information systems. A structured discourse is essential here, due to the practical relevance and the interdisciplinary nature of this issue. This paper presents a scientometric study on publications focusing on digital collaboration in cross-organizational and cross-border settings. We reviewed 80 articles published in six leading journals, EJIS, ISJ, ISR, JAIS, JMIS, and MISQ, during the 2000-2007 period. Besides the topics of investigation, subjects to analysis were the distribution of the articles by journal and year, authorship characteristics, and composition and regional distribution of the involved research teams. The findings indicate that most papers are focused on cross-organizational issues while cross-border research is very rare. Moreover, the structure of the research teams significantly differs across journals, and the number of international research teams is modest. Based on the findings, key observations and implications are discussed.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Digital cross-organizational and cross-border collaboration are emerging research issues. Significant drivers of this development are collaboration-related information systems. A structured discourse is essential here, due to the practical relevance and the interdisciplinary nature of this issue. This paper presents a scientometric study on publications focusing on digital collaboration in cross-organizational and cross-border settings. We reviewed 80 articles published in six leading journals, EJIS, ISJ, ISR, JAIS, JMIS, and MISQ, during the 2000-2007 period. Besides the topics of investigation, subjects to analysis were the distribution of the articles by journal and year, authorship characteristics, and composition and regional distribution of the involved research teams. The findings indicate that most papers are focused on cross-organizational issues while cross-border research is very rare. Moreover, the structure of the research teams significantly differs across journals, and the number of international research teams is modest. Based on the findings, key observations and implications are discussed.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Digital cross-organizational and cross-border collaboration are emerging research issues. Significant drivers of this development are collaboration-related information systems. A structured discourse is essential here, due to the practical relevance and the interdisciplinary nature of this issue. This paper presents a scientometric study on publications focusing on digital collaboration in cross-organizational and cross-border settings. We reviewed 80 articles published in six leading journals, EJIS, ISJ, ISR, JAIS, JMIS, and MISQ, during the 2000-2007 period. Besides the topics of investigation, subjects to analysis were the distribution of the articles by journal and year, authorship characteristics, and composition and regional distribution of the involved research teams. The findings indicate that most papers are focused on cross-organizational issues while cross-border research is very rare. Moreover, the structure of the research teams significantly differs across journals, and the number of international research teams is modest. Based on the findings, key observations and implications are discussed.",
"fno": "01-05-01",
"keywords": [],
"authors": [
{
"affiliation": null,
"fullName": "Maria Madlberger",
"givenName": "Maria",
"surname": "Madlberger",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Narcyz Roztocki",
"givenName": "Narcyz",
"surname": "Roztocki",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "hicss",
"isOpenAccess": true,
"showRecommendedArticles": true,
"showBuyMe": false,
"hasPdf": true,
"pubDate": "2009-12-01T00:00:00",
"pubType": "proceedings",
"pages": "1-10",
"year": "2009",
"issn": null,
"isbn": "978-0-7695-3450-3",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "01-04-05",
"articleId": "12OmNzyp5WG",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "01-05-02",
"articleId": "12OmNx6PivP",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/hicss/2011/9618/0/05718454",
"title": "Knowledge Sharability in Cross-Organizational Collaboration: An Exploratory Field Study",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2011/05718454/12OmNBkP3w5",
"parentPublication": {
"id": "proceedings/hicss/2011/9618/0",
"title": "2011 44th Hawaii International Conference on System Sciences",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hicss/2016/5670/0/5670a267",
"title": "Introduction to the Cross-Organizational and Cross-Border Collaboration Minitrack",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2016/5670a267/12OmNrIJqpR",
"parentPublication": {
"id": "proceedings/hicss/2016/5670/0",
"title": "2016 49th Hawaii International Conference on System Sciences (HICSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hicss/2014/2504/0/2504a159",
"title": "Introduction to Cross-Organizational and Cross-Border IS/IT Collaboration Minitrack",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2014/2504a159/12OmNs0C9AJ",
"parentPublication": {
"id": "proceedings/hicss/2014/2504/0",
"title": "2014 47th Hawaii International Conference on System Sciences (HICSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hicss/2012/4525/0/4525a274",
"title": "Introduction to Cross-Organizational and Cross-Border IS/IT Collaboration Minitrack",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2012/4525a274/12OmNxuXcAq",
"parentPublication": {
"id": "proceedings/hicss/2012/4525/0",
"title": "2012 45th Hawaii International Conference on System Sciences",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/circus/2009/4097/0/40970015",
"title": "Investigating Collaboration Driven by Requirements in Cross-Functional Software Teams",
"doi": null,
"abstractUrl": "/proceedings-article/circus/2009/40970015/12OmNy4IF82",
"parentPublication": {
"id": "proceedings/circus/2009/4097/0",
"title": "Requirements: Communication, Understanding and Softskills, Collaboration and Intercultural Issues on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hicss/2010/3869/0/01-06-01",
"title": "Digital Cross-Organizational Collaboration: A Metatriangulation Review",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2010/01-06-01/12OmNz5s0Nz",
"parentPublication": {
"id": "proceedings/hicss/2010/3869/0",
"title": "2010 43rd Hawaii International Conference on System Sciences",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/sp/2006/05/j5007",
"title": "Cross-Border Transaction Liability",
"doi": null,
"abstractUrl": "/magazine/sp/2006/05/j5007/13rRUxAAT5I",
"parentPublication": {
"id": "mags/sp",
"title": "IEEE Security & Privacy",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hicss/2007/2755/0/04076575",
"title": "Cross - Border Public Services: Analysis and Modeling",
"doi": null,
"abstractUrl": "/proceedings-article/hicss/2007/04076575/17D45VN31hJ",
"parentPublication": {
"id": "proceedings/hicss/2007/2755/0",
"title": "2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccsmt/2021/2063/0/206300a623",
"title": "Research on the Construction of Cross-border Payment System Based on Blockchain",
"doi": null,
"abstractUrl": "/proceedings-article/iccsmt/2021/206300a623/1E2w2drNB1C",
"parentPublication": {
"id": "proceedings/iccsmt/2021/2063/0",
"title": "2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ecit/2021/3873/0/387300a067",
"title": "Blockchain-based Cross-border E-business Payment Model",
"doi": null,
"abstractUrl": "/proceedings-article/ecit/2021/387300a067/1sZ3iSxOMes",
"parentPublication": {
"id": "proceedings/ecit/2021/3873/0",
"title": "2021 2nd International Conference on E-Commerce and Internet Technology (ECIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNzy7uOF",
"title": "Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004.",
"acronym": "iv",
"groupId": "1000370",
"volume": "0",
"displayVolume": "0",
"year": "2004",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNyS6RKR",
"doi": "10.1109/IV.2004.1320259",
"title": "Visualizing Interdisciplinary Citations to and from Information and Library Science Publications",
"normalizedTitle": "Visualizing Interdisciplinary Citations to and from Information and Library Science Publications",
"abstract": "Empirical investigations of citations to and from 150 journal articles published in the field of Information and Library Science (ILS) has enabled cross-mapping of the interdisciplinary evolution of the field. The publications were randomly drawn in six years between 1975 and 2000, with 25 articles each from the selected years. ANOVA tests reveal that although raw counts of self- and extradisciplinary- citations are not significantly different by year, they are different depending on whether it is from or to ILS. Nearly 30 disciplines hold mutual citations with ILS, and Computer Science, Communication, Management Science, Education, and Psychology are among those highly impact disciplines. Visualization further depicts the clustering of the citing and cited disciplines. In general out-degree citations contain richer set of nodes than those of in-degree, but overall, the mapping of the interdisciplinary scope of the ILS field provides convincing evidence for the interdisiciplinary nature of Library and Information Science.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Empirical investigations of citations to and from 150 journal articles published in the field of Information and Library Science (ILS) has enabled cross-mapping of the interdisciplinary evolution of the field. The publications were randomly drawn in six years between 1975 and 2000, with 25 articles each from the selected years. ANOVA tests reveal that although raw counts of self- and extradisciplinary- citations are not significantly different by year, they are different depending on whether it is from or to ILS. Nearly 30 disciplines hold mutual citations with ILS, and Computer Science, Communication, Management Science, Education, and Psychology are among those highly impact disciplines. Visualization further depicts the clustering of the citing and cited disciplines. In general out-degree citations contain richer set of nodes than those of in-degree, but overall, the mapping of the interdisciplinary scope of the ILS field provides convincing evidence for the interdisiciplinary nature of Library and Information Science.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Empirical investigations of citations to and from 150 journal articles published in the field of Information and Library Science (ILS) has enabled cross-mapping of the interdisciplinary evolution of the field. The publications were randomly drawn in six years between 1975 and 2000, with 25 articles each from the selected years. ANOVA tests reveal that although raw counts of self- and extradisciplinary- citations are not significantly different by year, they are different depending on whether it is from or to ILS. Nearly 30 disciplines hold mutual citations with ILS, and Computer Science, Communication, Management Science, Education, and Psychology are among those highly impact disciplines. Visualization further depicts the clustering of the citing and cited disciplines. In general out-degree citations contain richer set of nodes than those of in-degree, but overall, the mapping of the interdisciplinary scope of the ILS field provides convincing evidence for the interdisiciplinary nature of Library and Information Science.",
"fno": "21770972",
"keywords": [],
"authors": [
{
"affiliation": "Catholic University of America",
"fullName": "Rong Tang",
"givenName": "Rong",
"surname": "Tang",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "iv",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2004-07-01T00:00:00",
"pubType": "proceedings",
"pages": "972-977",
"year": "2004",
"issn": "1093-9547",
"isbn": "0-7695-2177-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "21770965",
"articleId": "12OmNqG0SXh",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "21770978",
"articleId": "12OmNy3Agq5",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/step/2002/1878/0/18780046",
"title": "Thirteen Knights and the Seven-headed Dragon: an Interdisciplinary Software Engineering Framework",
"doi": null,
"abstractUrl": "/proceedings-article/step/2002/18780046/12OmNButq4d",
"parentPublication": {
"id": "proceedings/step/2002/1878/0",
"title": "Proceedings 10th International Workshop on Software Technology and Engineering Practice",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2009/5340/0/3877a171",
"title": "A Protocol for Exchanging Scientific Citations",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2009/3877a171/12OmNvDZEWO",
"parentPublication": {
"id": "proceedings/e-science/2009/5340/0",
"title": "2009 5th IEEE International Conference on e-Science (e-Science 2009)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2011/468/0/06143043",
"title": "Teaching and assessing an interdisciplinary science of design pilot course",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2011/06143043/12OmNwcCIWv",
"parentPublication": {
"id": "proceedings/fie/2011/468/0",
"title": "2011 Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2007/1083/0/04418225",
"title": "Special session - Applying theories of interdisciplinary collaboration in research and teaching practice",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2007/04418225/12OmNyQGRYD",
"parentPublication": {
"id": "proceedings/fie/2007/1083/0",
"title": "2007 37th Annual Frontiers in Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2018/7202/0/720200a434",
"title": "Visualizing Design Process by Using Lean UX to Improve Interdisciplinary Team's Effectiveness – A Case Study",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2018/720200a434/17D45WHONs1",
"parentPublication": {
"id": "proceedings/iv/2018/7202/0",
"title": "2018 22nd International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2018/1174/0/08658461",
"title": "Introduction to Computing: Interdisciplinary Course Design",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2018/08658461/18j9oqs740E",
"parentPublication": {
"id": "proceedings/fie/2018/1174/0",
"title": "2018 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbar/2021/9565/0/956500a075",
"title": "A Decision Support Method for Interdisciplinary Major in Colleges and Universities Based on Natural Language Processing and Data Mining",
"doi": null,
"abstractUrl": "/proceedings-article/icbar/2021/956500a075/1BBySLFVD44",
"parentPublication": {
"id": "proceedings/icbar/2021/9565/0",
"title": "2021 International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09733942",
"title": "Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09733942/1BJIbG1OGqc",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2019/1547/0/154700a384",
"title": "Measuring the Interdisciplinary Degree of Information Behavior Research",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2019/154700a384/1ckrFa3BoWc",
"parentPublication": {
"id": "proceedings/jcdl/2019/1547/0",
"title": "2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2019/1547/0/154700a120",
"title": "Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2019/154700a120/1ckrHKT4bgA",
"parentPublication": {
"id": "proceedings/jcdl/2019/1547/0",
"title": "2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNxHrym1",
"title": "2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
"acronym": "asonam",
"groupId": "1002866",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNyoiYVM",
"doi": "10.1109/ASONAM.2016.7752307",
"title": "The scientometrics of successful women in science",
"normalizedTitle": "The scientometrics of successful women in science",
"abstract": "This paper examines the effects of gender differences in collaboration on research outcomes. We analyzed network characteristics of seventeen medical research institutions that are Clinical and Translational Science Awardees (CTSA) to determine if network connectivity characteristics have the potential to help mitigate the performance gap between the sexes. We determined betweenness centrality to identify well-connected researchers. Then we used clustering coefficient to determine how tightly connected their collaborators were with each other. We correlate these scores with productivity (number of total publications for each author), and h-index (the number of papers h for which an author has h citations). We also provide data on how network characteristics vary by role for each gender studied. Our results indicate that being well connected is more highly correlated with success for women than men for most of the institutions we studied. We believe these results can be leveraged to improve success rates for women in the future.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper examines the effects of gender differences in collaboration on research outcomes. We analyzed network characteristics of seventeen medical research institutions that are Clinical and Translational Science Awardees (CTSA) to determine if network connectivity characteristics have the potential to help mitigate the performance gap between the sexes. We determined betweenness centrality to identify well-connected researchers. Then we used clustering coefficient to determine how tightly connected their collaborators were with each other. We correlate these scores with productivity (number of total publications for each author), and h-index (the number of papers h for which an author has h citations). We also provide data on how network characteristics vary by role for each gender studied. Our results indicate that being well connected is more highly correlated with success for women than men for most of the institutions we studied. We believe these results can be leveraged to improve success rates for women in the future.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper examines the effects of gender differences in collaboration on research outcomes. We analyzed network characteristics of seventeen medical research institutions that are Clinical and Translational Science Awardees (CTSA) to determine if network connectivity characteristics have the potential to help mitigate the performance gap between the sexes. We determined betweenness centrality to identify well-connected researchers. Then we used clustering coefficient to determine how tightly connected their collaborators were with each other. We correlate these scores with productivity (number of total publications for each author), and h-index (the number of papers h for which an author has h citations). We also provide data on how network characteristics vary by role for each gender studied. Our results indicate that being well connected is more highly correlated with success for women than men for most of the institutions we studied. We believe these results can be leveraged to improve success rates for women in the future.",
"fno": "07752307",
"keywords": [
"Productivity",
"Collaboration",
"Correlation",
"Bibliometrics",
"Indexes",
"Engineering Profession",
"Informatics",
"Network Analysis",
"Gender Gap",
"Scientometrics",
"Collaboration",
"Clinical And Translational Science Awards"
],
"authors": [
{
"affiliation": "Health Informatics and Information Management, University of Tennessee Heal Science Center, 920 Madison Avenue Suite 518N, Memphis, TN 38163",
"fullName": "Charisse Madlock-Brown",
"givenName": "Charisse",
"surname": "Madlock-Brown",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Interdisciplinary Graduate Program in Informatics, School of Library and Information Science, University of Iowa, 3087 Main Library, Iowa City, Iowa 52242",
"fullName": "David Eichmann",
"givenName": "David",
"surname": "Eichmann",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "asonam",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-08-01T00:00:00",
"pubType": "proceedings",
"pages": "654-660",
"year": "2016",
"issn": null,
"isbn": "978-1-5090-2846-7",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "07752306",
"articleId": "12OmNB8Cj9P",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07752308",
"articleId": "12OmNvo67AN",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/fie/2011/468/0/06142743",
"title": "Representation of women and perceptions of support in engineering",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2011/06142743/12OmNvzJFZW",
"parentPublication": {
"id": "proceedings/fie/2011/468/0",
"title": "2011 Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/co/2014/10/mco2014100090",
"title": "Attracting and Retaining Women in Computing",
"doi": null,
"abstractUrl": "/magazine/co/2014/10/mco2014100090/13rRUyekJ11",
"parentPublication": {
"id": "mags/co",
"title": "Computer",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2018/1174/0/08658572",
"title": "Encouraging Women to Pursue a Computer Science Career in the Context of a Third World Country",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2018/08658572/18j964VcuIM",
"parentPublication": {
"id": "proceedings/fie/2018/1174/0",
"title": "2018 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2018/1174/0/08658768",
"title": "Exploring women’s motivations to study computer science",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2018/08658768/18j9of5AFaw",
"parentPublication": {
"id": "proceedings/fie/2018/1174/0",
"title": "2018 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/geicse/2022/9294/0/929400a035",
"title": "Retaining Women in Computer Science: the Good, the Bad and the Ugly Sides",
"doi": null,
"abstractUrl": "/proceedings-article/geicse/2022/929400a035/1FRKuxUNt0Q",
"parentPublication": {
"id": "proceedings/geicse/2022/9294/0",
"title": "2022 IEEE/ACM 3rd International Workshop on Gender Equality, Diversity and Inclusion in Software Engineering (GEICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ge/2019/2245/0/224500a001",
"title": "The Underrepresentation of Women in the Software Industry: Thoughts from Career-Changing Women",
"doi": null,
"abstractUrl": "/proceedings-article/ge/2019/224500a001/1cTJe52hyLu",
"parentPublication": {
"id": "proceedings/ge/2019/2245/0",
"title": "2019 IEEE/ACM 2nd International Workshop on Gender Equality in Software Engineering (GE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ge/2019/2245/0/224500a037",
"title": "Parent in Science: The Impact of Parenthood on the Scientific Career in Brazil",
"doi": null,
"abstractUrl": "/proceedings-article/ge/2019/224500a037/1cTJesVI4Cc",
"parentPublication": {
"id": "proceedings/ge/2019/2245/0",
"title": "2019 IEEE/ACM 2nd International Workshop on Gender Equality in Software Engineering (GE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2019/1746/0/09028496",
"title": "Assessing the Impact of Counterfactual Thinking on the Career Motivation of Women Engineers",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2019/09028496/1iffsIblIKA",
"parentPublication": {
"id": "proceedings/fie/2019/1746/0",
"title": "2019 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cs/2021/03/09459455",
"title": "The COVID-19 Pandemic is Widening the Gap for Women in STEM",
"doi": null,
"abstractUrl": "/magazine/cs/2021/03/09459455/1uvzYmPzOi4",
"parentPublication": {
"id": "mags/cs",
"title": "Computing in Science & Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/respect/2021/4905/0/09620629",
"title": "Female Scholars in Computer Science: The Role of Family and Other Factors in Achieving Academic Success",
"doi": null,
"abstractUrl": "/proceedings-article/respect/2021/09620629/1yXuL904CZy",
"parentPublication": {
"id": "proceedings/respect/2021/4905/0",
"title": "2021 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "17D45VtKipN",
"title": "2017 IEEE International Conference on Big Data (Big Data)",
"acronym": "big-data",
"groupId": "1802964",
"volume": "0",
"displayVolume": "0",
"year": "2017",
"__typename": "ProceedingType"
},
"article": {
"id": "17D45VsBTTI",
"doi": "10.1109/BigData.2017.8258573",
"title": "Discovering the interdisciplinary nature of big data research",
"normalizedTitle": "Discovering the interdisciplinary nature of big data research",
"abstract": "The study presented in this poster aims to address the paucity of studies examining the interdisciplinary nature of Big Data research. Using bibliometric records of articles downloaded from the Web of Science (WoS), this study utilizes the co-occurrence data between Subject Categories (SCs) related to Big Data research to discover the structure and pattern of the interdisciplinary network; its distribution and evolution over time; and the structural communities of interdisciplinary collaboration. The study also provides visualizations of these interdisciplinary networks. Additionally, this study measures the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of SCs of the related research publications using Stirling's Diversity Index and Specialization Index.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The study presented in this poster aims to address the paucity of studies examining the interdisciplinary nature of Big Data research. Using bibliometric records of articles downloaded from the Web of Science (WoS), this study utilizes the co-occurrence data between Subject Categories (SCs) related to Big Data research to discover the structure and pattern of the interdisciplinary network; its distribution and evolution over time; and the structural communities of interdisciplinary collaboration. The study also provides visualizations of these interdisciplinary networks. Additionally, this study measures the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of SCs of the related research publications using Stirling's Diversity Index and Specialization Index.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The study presented in this poster aims to address the paucity of studies examining the interdisciplinary nature of Big Data research. Using bibliometric records of articles downloaded from the Web of Science (WoS), this study utilizes the co-occurrence data between Subject Categories (SCs) related to Big Data research to discover the structure and pattern of the interdisciplinary network; its distribution and evolution over time; and the structural communities of interdisciplinary collaboration. The study also provides visualizations of these interdisciplinary networks. Additionally, this study measures the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of SCs of the related research publications using Stirling's Diversity Index and Specialization Index.",
"fno": "08258573",
"keywords": [
"Big Data",
"Collaboration",
"STEM",
"Indexes",
"Bibliometrics",
"Computer Science",
"Data Visualization",
"Big Data",
"Interdisciplinary Collaboration",
"Network Structure And Patterns",
"Visualization",
"Interdisciplinarity",
"Measures",
"Network Analysis"
],
"authors": [
{
"affiliation": "School of Information, Kent State University, Kent, USA",
"fullName": "Yin Zhang",
"givenName": "Yin",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Information Management, Wuhan University, Wuhan, China",
"fullName": "Jiming Hu",
"givenName": "Jiming",
"surname": "Hu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "big-data",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2017-12-01T00:00:00",
"pubType": "proceedings",
"pages": "4875-4877",
"year": "2017",
"issn": null,
"isbn": "978-1-5386-2715-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "08258572",
"articleId": "17D45WHONsx",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08258574",
"articleId": "17D45XDIXUd",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/bigdata-congress/2015/7278/0/07207228",
"title": "Meta Data: Big Data Research Evolving across Disciplines, Players, and Topics",
"doi": null,
"abstractUrl": "/proceedings-article/bigdata-congress/2015/07207228/12OmNxwENJ8",
"parentPublication": {
"id": "proceedings/bigdata-congress/2015/7278/0",
"title": "2015 IEEE International Congress on Big Data (BigData Congress)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2014/5666/0/07004345",
"title": "Why name ambiguity resolution matters for scholarly big data research",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2014/07004345/12OmNyuPKVq",
"parentPublication": {
"id": "proceedings/big-data/2014/5666/0",
"title": "2014 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpcc-smartcity-dss/2018/6614/0/661400a313",
"title": "Detecting Research Focus and Research Fronts in the Medical Big Data Field Using Co-word and Co-citation Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/hpcc-smartcity-dss/2018/661400a313/183rAfexDsE",
"parentPublication": {
"id": "proceedings/hpcc-smartcity-dss/2018/6614/0",
"title": "2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eitt/2021/2757/0/275700a088",
"title": "Factors Influencing K-12 STEM Teachers' Interdisciplinary Teaching Competency",
"doi": null,
"abstractUrl": "/proceedings-article/eitt/2021/275700a088/1AFsmmWS0GA",
"parentPublication": {
"id": "proceedings/eitt/2021/2757/0",
"title": "2021 Tenth International Conference of Educational Innovation through Technology (EITT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09733942",
"title": "Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09733942/1BJIbG1OGqc",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/beliv/2022/9629/0/962900a028",
"title": "An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper",
"doi": null,
"abstractUrl": "/proceedings-article/beliv/2022/962900a028/1J6hSbSTvy0",
"parentPublication": {
"id": "proceedings/beliv/2022/9629/0",
"title": "2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2019/1547/0/154700a384",
"title": "Measuring the Interdisciplinary Degree of Information Behavior Research",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2019/154700a384/1ckrFa3BoWc",
"parentPublication": {
"id": "proceedings/jcdl/2019/1547/0",
"title": "2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbdie/2020/5900/0/09150210",
"title": "Research Status of Big Data and Education Informatization in China — Study Based on Biliometric and Content Analysis (2010–2019)",
"doi": null,
"abstractUrl": "/proceedings-article/icbdie/2020/09150210/1lPGLzHGeJy",
"parentPublication": {
"id": "proceedings/icbdie/2020/5900/0",
"title": "2020 International Conference on Big Data and Informatization Education (ICBDIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2020/8961/0/09273976",
"title": "An Innovative Interdisciplinary Undergraduate Data Science Program: Pathways and Experience",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2020/09273976/1phRGqLV9Ru",
"parentPublication": {
"id": "proceedings/fie/2020/8961/0",
"title": "2020 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbdss/2020/9751/0/975100a207",
"title": "Current Situation and Countermeasure Analysis of Big Data Network Public Opinion Research in China and Abroad Based on Bibliometrics : —Taking the study of Internet public opinion in universities as an example",
"doi": null,
"abstractUrl": "/proceedings-article/icbdss/2020/975100a207/1tROzbIm5uU",
"parentPublication": {
"id": "proceedings/icbdss/2020/9751/0",
"title": "2020 International Conference on Big Data and Social Sciences (ICBDSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1s645BaTzVu",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"acronym": "big-data",
"groupId": "1802964",
"volume": "0",
"displayVolume": "0",
"year": "2020",
"__typename": "ProceedingType"
},
"article": {
"id": "1s64Bs1mh6E",
"doi": "10.1109/BigData50022.2020.9377970",
"title": "A bibliometric network analysis of Deep Learning publications applied into legal documents",
"normalizedTitle": "A bibliometric network analysis of Deep Learning publications applied into legal documents",
"abstract": "Deep Learning has been gradually adopted as the main methodology to perform Natural Language Processing tasks on legal documents. In this work we provide a bibliometric network analysis of Deep Learning publications (formerly Neural Networks) applied to the analysis of legal documents. Our study includes a total sample of 138 works published between 1987 and 2020 that used DL as primary methodology. We focused on three specific objectives: identification of the journals with more publications on the subject, a co-authorship network analysis, and an examination of the most cited works. Our results show that the publications are concentrated in a small number of specialized journals on the topic, and consequently, the number of works that use DL methodologies in the legal context is smaller compared to other areas. The co-authorship network analysis reveals four broad clusters of researchers that are time-dependent concentrated into Connectionism (1), Neural Networks (1) and Deep Learning (2). Our analysis of highly cited publications delivered 10 articles with two particular authors that centralize the network. Finally, we have found that collaboration between groups of researchers from different areas is minimal, showing a window of opportunity to increase interdisciplinary research, particularly between computer and legal research groups.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep Learning has been gradually adopted as the main methodology to perform Natural Language Processing tasks on legal documents. In this work we provide a bibliometric network analysis of Deep Learning publications (formerly Neural Networks) applied to the analysis of legal documents. Our study includes a total sample of 138 works published between 1987 and 2020 that used DL as primary methodology. We focused on three specific objectives: identification of the journals with more publications on the subject, a co-authorship network analysis, and an examination of the most cited works. Our results show that the publications are concentrated in a small number of specialized journals on the topic, and consequently, the number of works that use DL methodologies in the legal context is smaller compared to other areas. The co-authorship network analysis reveals four broad clusters of researchers that are time-dependent concentrated into Connectionism (1), Neural Networks (1) and Deep Learning (2). Our analysis of highly cited publications delivered 10 articles with two particular authors that centralize the network. Finally, we have found that collaboration between groups of researchers from different areas is minimal, showing a window of opportunity to increase interdisciplinary research, particularly between computer and legal research groups.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Deep Learning has been gradually adopted as the main methodology to perform Natural Language Processing tasks on legal documents. In this work we provide a bibliometric network analysis of Deep Learning publications (formerly Neural Networks) applied to the analysis of legal documents. Our study includes a total sample of 138 works published between 1987 and 2020 that used DL as primary methodology. We focused on three specific objectives: identification of the journals with more publications on the subject, a co-authorship network analysis, and an examination of the most cited works. Our results show that the publications are concentrated in a small number of specialized journals on the topic, and consequently, the number of works that use DL methodologies in the legal context is smaller compared to other areas. The co-authorship network analysis reveals four broad clusters of researchers that are time-dependent concentrated into Connectionism (1), Neural Networks (1) and Deep Learning (2). Our analysis of highly cited publications delivered 10 articles with two particular authors that centralize the network. Finally, we have found that collaboration between groups of researchers from different areas is minimal, showing a window of opportunity to increase interdisciplinary research, particularly between computer and legal research groups.",
"fno": "09377970",
"keywords": [
"Citation Analysis",
"Learning Artificial Intelligence",
"Natural Language Processing",
"Neural Nets",
"Legal Research Groups",
"Bibliometric Network Analysis",
"Deep Learning Publications",
"Legal Documents",
"Main Methodology",
"Neural Networks",
"Primary Methodology",
"Coauthorship Network Analysis",
"Cited Works",
"DL Methodologies",
"Legal Context",
"Highly Cited Publications",
"Natural Language Processing Tasks",
"Deep Learning",
"Text Analysis",
"Law",
"Bibliometrics",
"Neural Networks",
"Collaboration",
"Big Data",
"Deep Learning",
"Neural Networks",
"Bibliometric",
"Legal",
"Applications"
],
"authors": [
{
"affiliation": "Universidade Federal do Rio Grande do Sul (EA/PPGA),Porto Alegre,Brazil",
"fullName": "Alfredo Montelongo",
"givenName": "Alfredo",
"surname": "Montelongo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Fundação Getulio Vargas (FGV/EAESP),São Paulo,Brazil",
"fullName": "João Luiz Becker",
"givenName": "João Luiz",
"surname": "Becker",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "big-data",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2020-12-01T00:00:00",
"pubType": "proceedings",
"pages": "2131-2138",
"year": "2020",
"issn": null,
"isbn": "978-1-7281-6251-5",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "09378313",
"articleId": "1s64lgMpP7a",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09378201",
"articleId": "1s64pYxdy8w",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cso/2012/1365/0/06274817",
"title": "An Overview of Publications on Artificial Intelligence Research: A Quantitative Analysis on Recent Papers",
"doi": null,
"abstractUrl": "/proceedings-article/cso/2012/06274817/12OmNAlvHCR",
"parentPublication": {
"id": "proceedings/cso/2012/1365/0",
"title": "2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2006/354/0/04119099",
"title": "Bibliometric impact measures leveraging topic analysis",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2006/04119099/12OmNvTBB77",
"parentPublication": {
"id": "proceedings/jcdl/2006/354/0",
"title": "2006 IEEE/ACM 6th Joint Conference on Digital Libraries",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2010/06/mex2010060059",
"title": "A Bibliographic Analysis of IEEE Intelligent Systems Publications",
"doi": null,
"abstractUrl": "/magazine/ex/2010/06/mex2010060059/13rRUyekJ1Y",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09733942",
"title": "Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09733942/1BJIbG1OGqc",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/istm/2022/7116/0/711600a007",
"title": "Interpretable Text Classification in Legal Contract Documents using Tsetlin Machines",
"doi": null,
"abstractUrl": "/proceedings-article/istm/2022/711600a007/1HJzGIZp65G",
"parentPublication": {
"id": "proceedings/istm/2022/7116/0",
"title": "2022 International Symposium on the Tsetlin Machine (ISTM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccbd/2022/5716/0/10080528",
"title": "Bibliometric Analysis of Chinese Journals of Religion Between 1994 and 2021",
"doi": null,
"abstractUrl": "/proceedings-article/iccbd/2022/10080528/1LSP3acSCGI",
"parentPublication": {
"id": "proceedings/iccbd/2022/5716/0",
"title": "2022 5th International Conference on Computing and Big Data (ICCBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/clei/2018/0437/0/043700a519",
"title": "Semantically Identifying Regional-Indexed Publications, a Web-Exploring Approach",
"doi": null,
"abstractUrl": "/proceedings-article/clei/2018/043700a519/1cdP1HD5GE0",
"parentPublication": {
"id": "proceedings/clei/2018/0437/0",
"title": "2018 XLIV Latin American Computer Conference (CLEI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2020/9012/0/901200a775",
"title": "Tasks performed in the legal domain through Deep Learning: A bibliometric review (1987–2020)",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2020/901200a775/1rgGhxwYuOI",
"parentPublication": {
"id": "proceedings/icdmw/2020/9012/0",
"title": "2020 International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icemme/2020/9144/0/914400a132",
"title": "A Bibliometric Analysis of Research on Auditing for Sustainable Corporate Governance",
"doi": null,
"abstractUrl": "/proceedings-article/icemme/2020/914400a132/1tV98aZHvMI",
"parentPublication": {
"id": "proceedings/icemme/2020/9144/0",
"title": "2020 2nd International Conference on Economic Management and Model Engineering (ICEMME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2020/6406/0/640600a358",
"title": "Bibliometric Visualization: An Application in Tourism Logo Research Using CiteSpace",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2020/640600a358/1x3kFQSor4Y",
"parentPublication": {
"id": "proceedings/icisce/2020/6406/0",
"title": "2020 7th International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "12OmNwB2dUd",
"title": "2016 IEEE Symposium on 3D User Interfaces (3DUI)",
"acronym": "3dui",
"groupId": "1001623",
"volume": "0",
"displayVolume": "0",
"year": "2016",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNC4eSwS",
"doi": "10.1109/3DUI.2016.7460052",
"title": "A hybrid projection to widen the vertical field of view with large screens to improve the perception of personal space in architectural project review",
"normalizedTitle": "A hybrid projection to widen the vertical field of view with large screens to improve the perception of personal space in architectural project review",
"abstract": "In this paper, we suggest using a hybrid projection to increase the vertical geometric field of view without incurring large deformations to preserve distance perception and to allow the seeing of the surrounding ground. We have conducted an experiment in furnished and unfurnished houses to evaluate the perception of distances and the spatial comprehension. Results show that the hybrid projection improves the perception of surrounding ground which leads to an improvement in the spatial comprehension. Moreover, it preserves the perception of distances and sizes by providing a performance similar to the perspective one in the task of distance estimation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we suggest using a hybrid projection to increase the vertical geometric field of view without incurring large deformations to preserve distance perception and to allow the seeing of the surrounding ground. We have conducted an experiment in furnished and unfurnished houses to evaluate the perception of distances and the spatial comprehension. Results show that the hybrid projection improves the perception of surrounding ground which leads to an improvement in the spatial comprehension. Moreover, it preserves the perception of distances and sizes by providing a performance similar to the perspective one in the task of distance estimation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we suggest using a hybrid projection to increase the vertical geometric field of view without incurring large deformations to preserve distance perception and to allow the seeing of the surrounding ground. We have conducted an experiment in furnished and unfurnished houses to evaluate the perception of distances and the spatial comprehension. Results show that the hybrid projection improves the perception of surrounding ground which leads to an improvement in the spatial comprehension. Moreover, it preserves the perception of distances and sizes by providing a performance similar to the perspective one in the task of distance estimation.",
"fno": "07460052",
"keywords": [
"Hafnium Compounds",
"Three Dimensional Displays",
"Estimation",
"Electronic Mail",
"Context",
"Rendering Computer Graphics",
"Visualization",
"I 3 7 Computer Graphics Three Dimensional Graphics And Realism Virtual Reality",
"H 5 1 Information Interfaces And Presentation Multimedia Information Systems Artificial Augmented And Virtual Realities"
],
"authors": [
{
"affiliation": "University of Strasbourg, France",
"fullName": "Sabah Boustila",
"givenName": "Sabah",
"surname": "Boustila",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Strasbourg, France",
"fullName": "Antonio Capobianco",
"givenName": "Antonio",
"surname": "Capobianco",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Strasbourg, France",
"fullName": "Dominique Bechmann",
"givenName": "Dominique",
"surname": "Bechmann",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Strasbourg, France",
"fullName": "Olivier Génevaux",
"givenName": "Olivier",
"surname": "Génevaux",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "3dui",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2016-03-01T00:00:00",
"pubType": "proceedings",
"pages": "191-200",
"year": "2016",
"issn": null,
"isbn": "978-1-5090-0842-1",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "07460051",
"articleId": "12OmNqJHFp9",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07460053",
"articleId": "12OmNBubORd",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/vr/2016/0836/0/07504702",
"title": "New hybrid projection to widen the vertical field of view with large screen to improve the perception of personal space in architectural project review",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2016/07504702/12OmNrkT7wJ",
"parentPublication": {
"id": "proceedings/vr/2016/0836/0",
"title": "2016 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pacificvis/2013/4797/0/06596131",
"title": "Evaluation of Depth of Field for depth perception in DVR",
"doi": null,
"abstractUrl": "/proceedings-article/pacificvis/2013/06596131/12OmNx76TOn",
"parentPublication": {
"id": "proceedings/pacificvis/2013/4797/0",
"title": "2013 IEEE Pacific Visualization Symposium (PacificVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dui/2015/6886/0/07131756",
"title": "Distance perception during cooperative virtual locomotion",
"doi": null,
"abstractUrl": "/proceedings-article/3dui/2015/07131756/12OmNy49sEA",
"parentPublication": {
"id": "proceedings/3dui/2015/6886/0",
"title": "2015 IEEE Symposium on 3D User Interfaces (3DUI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ieee-vis/1995/7187/0/71870011",
"title": "Interactive Maximum Projection Volume Rendering",
"doi": null,
"abstractUrl": "/proceedings-article/ieee-vis/1995/71870011/12OmNzZmZv2",
"parentPublication": {
"id": "proceedings/ieee-vis/1995/7187/0",
"title": "Visualization Conference, IEEE",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2018/3365/0/08446481",
"title": "Light Projection-Induced Illusion for Controlling Object Color",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446481/13bd1AITnaR",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2007/06/v1344",
"title": "Enhancing Depth-Perception with Flexible Volumetric Halos",
"doi": null,
"abstractUrl": "/journal/tg/2007/06/v1344/13rRUygT7ss",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2009/3943/0/04811057",
"title": "Immersive Rear Projection on Curved Screens",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2009/04811057/1lssAh0wwUg",
"parentPublication": {
"id": "proceedings/vr/2009/3943/0",
"title": "2009 IEEE Virtual Reality Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2020/8508/0/850800a080",
"title": "Can Retinal Projection Displays Improve Spatial Perception in Augmented Reality?",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2020/850800a080/1pysvYTZF6w",
"parentPublication": {
"id": "proceedings/ismar/2020/8508/0",
"title": "2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2021/1838/0/255600a122",
"title": "Augmented Reality for Maritime Navigation Assistance - Egocentric Depth Perception in Large Distance Outdoor Environments",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2021/255600a122/1tuB9Rs5D2M",
"parentPublication": {
"id": "proceedings/vr/2021/1838/0",
"title": "2021 IEEE Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2021/0158/0/015800a517",
"title": "Scan&Paint: Image-based Projection Painting",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2021/015800a517/1yeD6SIJJrG",
"parentPublication": {
"id": "proceedings/ismar/2021/0158/0",
"title": "2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1lgop3uO3QI",
"title": "2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)",
"acronym": "compsac",
"groupId": "1000143",
"volume": "2",
"displayVolume": "2",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1cYipFcvorK",
"doi": "10.1109/COMPSAC.2019.10180",
"title": "A Proposal of SDN-FIT System to Evaluate Wide-Area Distributed Applications Based on Exhaustive FIT Scenario Generation",
"normalizedTitle": "A Proposal of SDN-FIT System to Evaluate Wide-Area Distributed Applications Based on Exhaustive FIT Scenario Generation",
"abstract": "A wide-area distributed application is affected by network failure due to natural disasters because the servers on which the application operates are distributed geographically in a wide-area. Failure Injection Testing (FIT) is a method for verifying fault tolerance of widely distributed applications. In this paper, by limiting network failures to connection line, whole FIT scenarios are generated and exhaustive evaluation of fault tolerance is performed. Authors evaluate the visualization method of performance data obtained from this evaluation and the reduction of the evaluation cost by the proposed method.",
"abstracts": [
{
"abstractType": "Regular",
"content": "A wide-area distributed application is affected by network failure due to natural disasters because the servers on which the application operates are distributed geographically in a wide-area. Failure Injection Testing (FIT) is a method for verifying fault tolerance of widely distributed applications. In this paper, by limiting network failures to connection line, whole FIT scenarios are generated and exhaustive evaluation of fault tolerance is performed. Authors evaluate the visualization method of performance data obtained from this evaluation and the reduction of the evaluation cost by the proposed method.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "A wide-area distributed application is affected by network failure due to natural disasters because the servers on which the application operates are distributed geographically in a wide-area. Failure Injection Testing (FIT) is a method for verifying fault tolerance of widely distributed applications. In this paper, by limiting network failures to connection line, whole FIT scenarios are generated and exhaustive evaluation of fault tolerance is performed. Authors evaluate the visualization method of performance data obtained from this evaluation and the reduction of the evaluation cost by the proposed method.",
"fno": "260702a036",
"keywords": [
"Data Visualisation",
"Disasters",
"Distributed Processing",
"Fault Tolerant Computing",
"Network Servers",
"Software Defined Networking",
"Network Failure",
"Fault Tolerance",
"Exhaustive Evaluation",
"SDN FIT System",
"Wide Area Distributed Application",
"Exhaustive FIT Scenario Generation",
"Failure Injection",
"Evaluation Cost Reduction",
"Visualization Method",
"Connection Line",
"Benchmark Testing",
"Data Visualization",
"Fault Tolerance",
"Fault Tolerant Systems",
"Software",
"Packet Loss",
"Distributed System Resilience Fault Injection Testing"
],
"authors": [
{
"affiliation": "Cybersecurity R&D Center National Institute of Informatics",
"fullName": "Hiroki Kashiwazaki",
"givenName": "Hiroki",
"surname": "Kashiwazaki",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Osaka University",
"fullName": "Shinnosuke Miura",
"givenName": "Shinnosuke",
"surname": "Miura",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Osaka University",
"fullName": "Shinji Shimojo",
"givenName": "Shinji",
"surname": "Shimojo",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "compsac",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-07-01T00:00:00",
"pubType": "proceedings",
"pages": "36-41",
"year": "2019",
"issn": "0730-3157",
"isbn": "978-1-7281-2607-4",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "260702a030",
"articleId": "1cYirSWdsGI",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "260702a042",
"articleId": "1cYiu9LwLJe",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ipdps/2012/4675/0/4675b240",
"title": "Scalable Distributed Consensus to Support MPI Fault Tolerance",
"doi": null,
"abstractUrl": "/proceedings-article/ipdps/2012/4675b240/12OmNqFJhOa",
"parentPublication": {
"id": "proceedings/ipdps/2012/4675/0",
"title": "Parallel and Distributed Processing Symposium, International",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/grid/2010/9347/0/05697961",
"title": "Analysis and modeling of time-correlated failures in large-scale distributed systems",
"doi": null,
"abstractUrl": "/proceedings-article/grid/2010/05697961/12OmNrkBwsz",
"parentPublication": {
"id": "proceedings/grid/2010/9347/0",
"title": "2010 11th IEEE/ACM International Conference on Grid Computing (GRID 2010)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dft/2014/6155/0/06962070",
"title": "Decreasing FIT with diverse triple modular redundancy in SRAM-based FPGAs",
"doi": null,
"abstractUrl": "/proceedings-article/dft/2014/06962070/12OmNvjQ8Vz",
"parentPublication": {
"id": "proceedings/dft/2014/6155/0",
"title": "2014 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdcs/2017/1792/0/1792c656",
"title": "A Self-Organizing Distributed and In-Band SDN Control Plane",
"doi": null,
"abstractUrl": "/proceedings-article/icdcs/2017/1792c656/12OmNzxPTIq",
"parentPublication": {
"id": "proceedings/icdcs/2017/1792/0",
"title": "2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/snpd/2018/5889/0/08441131",
"title": "SDN-SDWSN Controller Fault Tolerance Framework for Small to Medium Sized Networks",
"doi": null,
"abstractUrl": "/proceedings-article/snpd/2018/08441131/13bd1gzWkRc",
"parentPublication": {
"id": "proceedings/snpd/2018/5889/0",
"title": "2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/issrew/2021/2603/0/260300a427",
"title": "How Far Have We Come in Fault Tolerance for Distributed Graph Processing: A Quantitative Assessment of Fault Tolerance Effectiveness",
"doi": null,
"abstractUrl": "/proceedings-article/issrew/2021/260300a427/1AZOc0Lim9q",
"parentPublication": {
"id": "proceedings/issrew/2021/2603/0",
"title": "2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2020/7303/0/730300a607",
"title": "A Quantitative Evaluation of a Wide-Area Distributed System with SDN-FIT",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2020/730300a607/1nkDkXLkhGw",
"parentPublication": {
"id": "proceedings/compsac/2020/7303/0",
"title": "2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icoin/2021/9101/0/09333893",
"title": "A Proposal of Stochastic Quantitative Resilience Index Based on SLAs for Communication Lines",
"doi": null,
"abstractUrl": "/proceedings-article/icoin/2021/09333893/1qTrR1nqpuo",
"parentPublication": {
"id": "proceedings/icoin/2021/9101/0",
"title": "2021 International Conference on Information Networking (ICOIN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2021/2463/0/246300b449",
"title": "An Evaluation of Stochastic Quantitative Resilience Index Based on SLAs of Communication Lines",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2021/246300b449/1wLcuDAflGo",
"parentPublication": {
"id": "proceedings/compsac/2021/2463/0",
"title": "2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/srds/2021/3819/0/381900a099",
"title": "Enabling Low-Redundancy Proactive Fault Tolerance for Stream Machine Learning via Erasure Coding",
"doi": null,
"abstractUrl": "/proceedings-article/srds/2021/381900a099/1yJZcqlaDBK",
"parentPublication": {
"id": "proceedings/srds/2021/3819/0",
"title": "2021 40th International Symposium on Reliable Distributed Systems (SRDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1ehBy9p57Q4",
"title": "2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",
"acronym": "ithings-greencom-cpscom-smartdata",
"groupId": "1800308",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1ehBFLparPq",
"doi": "10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00136",
"title": "Delayed Best-Fit Task Scheduling to Reduce Energy Consumption in Cloud Data Centers",
"normalizedTitle": "Delayed Best-Fit Task Scheduling to Reduce Energy Consumption in Cloud Data Centers",
"abstract": "Reducing energy consumption of cloud data center is critical for its sustainable growth. We propose the delayed best-fit task-scheduling scheme that strategically delays the scheduling of tasks to the most energy-efficient servers of data centers to reduce its energy consumption. The proposed scheme uses static and dynamic thresholds on the power consumption increment an allocated task is associated with an assigned server to balance energy consumption and task completion time. The proposed scheme is tested on a real traffic trace from a Google data center and compared with best-fit and first-fit scheduling algorithms. We show that the proposed delayed best-fit task-scheduling scheme reduces data center energy consumption by 15% of that attained by the best-fit algorithm on the same trace, without compromising the average task completion time.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Reducing energy consumption of cloud data center is critical for its sustainable growth. We propose the delayed best-fit task-scheduling scheme that strategically delays the scheduling of tasks to the most energy-efficient servers of data centers to reduce its energy consumption. The proposed scheme uses static and dynamic thresholds on the power consumption increment an allocated task is associated with an assigned server to balance energy consumption and task completion time. The proposed scheme is tested on a real traffic trace from a Google data center and compared with best-fit and first-fit scheduling algorithms. We show that the proposed delayed best-fit task-scheduling scheme reduces data center energy consumption by 15% of that attained by the best-fit algorithm on the same trace, without compromising the average task completion time.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Reducing energy consumption of cloud data center is critical for its sustainable growth. We propose the delayed best-fit task-scheduling scheme that strategically delays the scheduling of tasks to the most energy-efficient servers of data centers to reduce its energy consumption. The proposed scheme uses static and dynamic thresholds on the power consumption increment an allocated task is associated with an assigned server to balance energy consumption and task completion time. The proposed scheme is tested on a real traffic trace from a Google data center and compared with best-fit and first-fit scheduling algorithms. We show that the proposed delayed best-fit task-scheduling scheme reduces data center energy consumption by 15% of that attained by the best-fit algorithm on the same trace, without compromising the average task completion time.",
"fno": "298000a729",
"keywords": [
"Cloud Computing",
"Computer Centres",
"Power Aware Computing",
"Power Consumption",
"Scheduling",
"Delayed Best Fit Task Scheduling",
"Cloud Data Center",
"Task Scheduling Scheme",
"Energy Efficient Servers",
"Power Consumption",
"Google Data Center",
"First Fit Scheduling Algorithms",
"Data Center Energy Consumption",
"Task Analysis",
"Data Centers",
"Servers",
"Energy Consumption",
"Power Demand",
"Processor Scheduling",
"Scheduling",
"Cloud Computing Data Center Energy Consumption Task Scheduling Delayed Best Fit Task Completion Time"
],
"authors": [
{
"affiliation": "New York Institute of Technology",
"fullName": "Ziqian Dong",
"givenName": "Ziqian",
"surname": "Dong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "New York Institute of Technology",
"fullName": "Wenjie Zhuang",
"givenName": "Wenjie",
"surname": "Zhuang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "New Jersey Institute of Technology",
"fullName": "Roberto Rojas-Cessa",
"givenName": "Roberto",
"surname": "Rojas-Cessa",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "ithings-greencom-cpscom-smartdata",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-07-01T00:00:00",
"pubType": "proceedings",
"pages": "729-736",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-2980-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "298000a719",
"articleId": "1ehBLA2fBFm",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "298000a737",
"articleId": "1ehBJlinnWg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/ccem/2017/2450/0/2450a047",
"title": "A Dynamic and Energy Efficient Greedy Scheduling Algorithm for Cloud Data Centers",
"doi": null,
"abstractUrl": "/proceedings-article/ccem/2017/2450a047/12OmNApLGny",
"parentPublication": {
"id": "proceedings/ccem/2017/2450/0",
"title": "2017 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgc/2013/5114/0/5114a026",
"title": "Energy-Aware Scheduling on Multiprocessor Platforms with Devices",
"doi": null,
"abstractUrl": "/proceedings-article/cgc/2013/5114a026/12OmNBr4eAL",
"parentPublication": {
"id": "proceedings/cgc/2013/5114/0",
"title": "2013 International Conference on Cloud and Green Computing (CGC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icnc/2015/6959/0/07069401",
"title": "Thermal-aware energy-efficient task scheduling for DVFS-enabled data centers",
"doi": null,
"abstractUrl": "/proceedings-article/icnc/2015/07069401/12OmNC1GujH",
"parentPublication": {
"id": "proceedings/icnc/2015/6959/0",
"title": "2015 International Conference on Computing, Networking and Communications (ICNC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/services/2013/5024/0/5024a397",
"title": "An Energy-Efficient Online Parallel Scheduling Algorithm for Cloud Data Centers",
"doi": null,
"abstractUrl": "/proceedings-article/services/2013/5024a397/12OmNrJ11FH",
"parentPublication": {
"id": "proceedings/services/2013/5024/0",
"title": "2013 IEEE World Congress on Services (SERVICES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nas/2013/5034/0/5034a041",
"title": "BFEPM: Best Fit Energy Prediction Modeling Based on CPU Utilization",
"doi": null,
"abstractUrl": "/proceedings-article/nas/2013/5034a041/12OmNrMZpFt",
"parentPublication": {
"id": "proceedings/nas/2013/5034/0",
"title": "2013 IEEE 8th International Conference on Networking, Architecture, and Storage (NAS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscc/2015/7194/0/07405605",
"title": "Offline first fit scheduling in smart grids",
"doi": null,
"abstractUrl": "/proceedings-article/iscc/2015/07405605/12OmNvStcBV",
"parentPublication": {
"id": "proceedings/iscc/2015/7194/0",
"title": "2015 IEEE Symposium on Computers and Communication (ISCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csci/2014/3010/2/3010b069",
"title": "Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud",
"doi": null,
"abstractUrl": "/proceedings-article/csci/2014/3010b069/12OmNzdoMOf",
"parentPublication": {
"id": "proceedings/csci/2014/3010/1",
"title": "2014 International Conference on Computational Science and Computational Intelligence (CSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cloudcom/2012/4511/0/06427545",
"title": "Green-aware workload scheduling in geographically distributed data centers",
"doi": null,
"abstractUrl": "/proceedings-article/cloudcom/2012/06427545/12OmNznkJRK",
"parentPublication": {
"id": "proceedings/cloudcom/2012/4511/0",
"title": "4th IEEE International Conference on Cloud Computing Technology and Science Proceedings",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/sc/2021/06/08673602",
"title": "A Learning Automata-Based Scheduling for Deadline Sensitive Task in The Cloud",
"doi": null,
"abstractUrl": "/journal/sc/2021/06/08673602/1zarSgp1Xva",
"parentPublication": {
"id": "trans/sc",
"title": "IEEE Transactions on Services Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2021/3574/0/357400a947",
"title": "A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2021/357400a947/1zxLkRj9Sus",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2021/3574/0",
"title": "2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
{
"proceeding": {
"id": "1iTvczdcyc0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"acronym": "cvprw",
"groupId": "8972688",
"volume": "0",
"displayVolume": "0",
"year": "2019",
"__typename": "ProceedingType"
},
"article": {
"id": "1iTvuQ1sGnm",
"doi": "10.1109/CVPRW.2019.00046",
"title": "SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images",
"normalizedTitle": "SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images",
"abstract": "Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms everyday, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms everyday, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms everyday, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.",
"fno": "250600a335",
"keywords": [
"Clothing Industry",
"Electronic Commerce",
"Image Processing",
"Learning Artificial Intelligence",
"Purchasing",
"Statistical Analysis",
"Textile Garments",
"Cold Start Problem",
"Visual Cues",
"Article Images",
"Rich Visual Information",
"Weakly Supervised Teacher Student Training Framework",
"Fashion Articles",
"Visual Data",
"Prior Purchase History",
"Shopping Platforms",
"Statistical Methods",
"E Commerce Fashion Industry",
"Clothes",
"Fashion Images",
"Visual Size",
"Weakly Supervised Learning",
"Size Net",
"Data Models",
"Visualization",
"Clothing",
"Predictive Models",
"Bayes Methods",
"History",
"Training"
],
"authors": [
{
"affiliation": "Zalando SE, Germany",
"fullName": "Nour Karessli",
"givenName": "Nour",
"surname": "Karessli",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Zalando SE, Germany",
"fullName": "Romain Guigourès",
"givenName": "Romain",
"surname": "Guigourès",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Zalando SE, Germany",
"fullName": "Reza Shirvany",
"givenName": "Reza",
"surname": "Shirvany",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "cvprw",
"isOpenAccess": false,
"showRecommendedArticles": true,
"showBuyMe": true,
"hasPdf": true,
"pubDate": "2019-06-01T00:00:00",
"pubType": "proceedings",
"pages": "335-343",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-2506-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"webExtras": [],
"adjacentArticles": {
"previous": {
"fno": "250600a326",
"articleId": "1iTvsy044o0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "250600a344",
"articleId": "1iTvvHUppkI",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2017/0457/0/0457f085",
"title": "Hard Mixtures of Experts for Large Scale Weakly Supervised Vision",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457f085/12OmNxTmHJR",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032a388",
"title": "Fashion Forward: Forecasting Visual Style in Fashion",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032a388/12OmNyoAA5L",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2017/1034/0/1034c268",
"title": "Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2017/1034c268/12OmNzAohUI",
"parentPublication": {
"id": "proceedings/iccvw/2017/1034/0",
"title": "2017 IEEE International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000h161",
"title": "Creating Capsule Wardrobes from Fashion Images",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000h161/17D45XDIXVQ",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200b046",
"title": "From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200b046/1BmJR2uYR6U",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956089",
"title": "FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956089/1IHqz3SBHIk",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300j045",
"title": "Personalized Fashion Design",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300j045/1hQqgvTQUOk",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800k0133",
"title": "From Paris to Berlin: Discovering Fashion Style Influences Around the World",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800k0133/1m3nMMxVEYg",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigmm/2020/9325/0/09232532",
"title": "Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling",
"doi": null,
"abstractUrl": "/proceedings-article/bigmm/2020/09232532/1o56B2bCTni",
"parentPublication": {
"id": "proceedings/bigmm/2020/9325/0",
"title": "2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/09/09325067",
"title": "Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise",
"doi": null,
"abstractUrl": "/journal/tg/2021/09/09325067/1qnQCmbSzUA",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"articleVideos": []
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.