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"title": "Yoga Asanas Pose Detection using Feature Level Fusion with Deep Learning-Based Model",
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"abstract": "Yoga is a healthful activity that started in India and has spread throughout the world, rejuvenating a man's physical, mental, and spiritual well-being. However, Yoga Image analysis is recent research based on Position identification. So Pose discovery procedures can recognize yoga postures and help people practise Yoga more precisely. But increasing dimensionality and segmentation problems lead to posture recognition is a complex problem. To resolve this, we propose a Deep learning algorithm that can be used to reliably identify numerous yoga asanas, as demonstrated in this paper. Since all the video data is raw, pre-processing is necessary before the model can be used. Objects are recognized using the YOLOv5 network in the pre-processed frames, and skeleton pose estimation is performed. Based on the identified objects, an algorithm makes a skeleton of a human in real-time. The skeletal position skeleton and objects detected by YOLOv5 are fused using feature-level fusion to derive the angles of the linkages in the human body. Based on Generative Adversarial Network (GAN) is implemented to predict the yoga postures, and these outputs are fed into the learning process. About 80% of the dataset was for training, while the residual 20% was used for testing purposes. The proposed system achieves high performance in identifying the Yoga Pose gestures accurately compared to other systems.",
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"abstract": "Paraplegia is a disability caused by impairment in motor or sensory functions of the lower limbs. Most paraplegic subjects use mechanical or motorised wheelchairs for their movement, however, this may limit the capability of patients to independently perform common activities of daily living (ADL). In this paper, a novel mixed reality (MR) enabled proprio and teleoperation framework for a humanoid robot is presented. The framework can be operated by a paraplegic person by using inputs from an MR headset. The framework enables varied and unscripted manipulation tasks in a realistic environment, combining navigation, perception, manipulation, and grasping. The impaired operator can make use of a wide range of interaction methods and tools, from direct teleoperation of the robot's full-body kinematics to performing grasping tasks or controlling the robot's mobile base. The adopted humanoid robot is the EVEr3 Humanoid Research Robot from Halodi Robotics, while the Oculus Rift S is chosen as MR headset. To demonstrate the potential of the proposed framework, a human subject study is presented. In this study, a home/workplace environment is rendered with MR by combining physical shelves and everyday objects, such as goods to be grasped, with simulated elements, such as the robot avatar and the control interface. A paraplegic subject is involved in the study. Results suggest that the proposed MR-enabled system improves the patient engagement and illusion of presence.",
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"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
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"abstract": "In this work we present a collaborative virtual reality application for distributed engineering tasks, including a simple avatar calibration method. Use cases for the application include CAD review, ergonomics analyses or virtual training. Full-body avatars with approximately natural motion characteristics are used to improve collaboration in terms of co-presence and embodiment. Through a calibration process, scaled avatars based on body measurements can be generated by users from within the VR application. For motion tracking, only the tracking capabilities of the VR devices used (head mounted display and hand controllers) are required, in order to achieve a low initialization effort for usage of the application. We demonstrate a simple, yet effective method for measuring the human body which requires these VR devices only. Tracking only the user's head and hands, we require inverse kinematics (IK) for avatar motion reconstruction. On the other hand, this requires a calibrated avatar, otherwise body postures and gestures will be misrepresented. In the first step of the demo, two users can create their calibrated avatar at the same time and then carry out a collaborative CAD review in the second step.",
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"content": "In this work we present a collaborative virtual reality application for distributed engineering tasks, including a simple avatar calibration method. Use cases for the application include CAD review, ergonomics analyses or virtual training. Full-body avatars with approximately natural motion characteristics are used to improve collaboration in terms of co-presence and embodiment. Through a calibration process, scaled avatars based on body measurements can be generated by users from within the VR application. For motion tracking, only the tracking capabilities of the VR devices used (head mounted display and hand controllers) are required, in order to achieve a low initialization effort for usage of the application. We demonstrate a simple, yet effective method for measuring the human body which requires these VR devices only. Tracking only the user's head and hands, we require inverse kinematics (IK) for avatar motion reconstruction. On the other hand, this requires a calibrated avatar, otherwise body postures and gestures will be misrepresented. In the first step of the demo, two users can create their calibrated avatar at the same time and then carry out a collaborative CAD review in the second step.",
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"affiliation": "Graduate School of Excellence advanced Manufacturing Engineering, University of Stuttgart, Germany",
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"affiliation": "Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany",
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"affiliation": "Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany",
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"affiliation": "Max Planck Institute for Biological Cybernetics, Tuebingen, Germany",
"fullName": "Adamantini Hatzipanayioti",
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"affiliation": "Amazon Research, Tuebingen, Germany",
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"affiliation": "Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany",
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"affiliation": "Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany",
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"doi": "10.1109/ICIA.2006.305885",
"title": "Ant Colony Optimization for the Stochastic Loader Problem",
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"abstract": "In 2004, Tang proposed a new NP-hard combinational optimization problem that frequently arises in practice - the loader problem (a transportation model). Two special cases of the problem (the restricted loader problem and the equal loader problem) and optimal solution strategy have been considered. In this paper, we extend Tang's model by proposing the stochastic quantity of load and unload at each station that make the model more applicable in practice. An ant colony optimization (ACO) algorithm is designed for solving the .stochastic loader problem. Two numerical examples are presented to illustrate the application of the developed model.",
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"Educational Institutions",
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"affiliation": "School of Management, Shandong University, Jinan, Shandong Province, China, pxzhao@126.com",
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"affiliation": "College of Finance, Shandong Institute of Light Industry, Jinan, Shandong Province, China, jmjj55@126.com",
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"doi": "10.1109/FSKD.2007.154",
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"abstract": "for a solution of a invex nonsmooth program- ming problem is introduced. An equivalent -approximated vector optimization problem is constructed by a modification of the objec- tive and the constraint functions in the orig- inal multiobjective programming problem and its equivalent -approximated vector optimiza- tion problem is proved. In this way, optimal- ity conditions for nonsmooth constrained multi- objective programming problems having gener- alized invex objective and constraint functions (with respect to the same function )are ob- tained. Key word: invex nonsmooth multiobjective programming ; -approximated vector opti- mization; (weak)efficient solution",
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"content": "for a solution of a invex nonsmooth program- ming problem is introduced. An equivalent -approximated vector optimization problem is constructed by a modification of the objec- tive and the constraint functions in the orig- inal multiobjective programming problem and its equivalent -approximated vector optimiza- tion problem is proved. In this way, optimal- ity conditions for nonsmooth constrained multi- objective programming problems having gener- alized invex objective and constraint functions (with respect to the same function )are ob- tained. Key word: invex nonsmooth multiobjective programming ; -approximated vector opti- mization; (weak)efficient solution",
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"affiliation": "Wuhan University of Science and Technology",
"fullName": "Shu-li Xu",
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"title": "A Methodology Combining Optimization and Simulation for Real Applications of the Stochastic Aircraft Recovery Problem",
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"abstract": "The Aircraft Recovery Problem appears when external events cause disruptions in a flight schedule. Thus in order to minimize the losses caused by the externalities, aircrafts must be reallocated (rescheduled) in the best possible way. The aim of this paper is to develop a suitable methodology that combines optimization techniques with a simulation approach to tackle the so-called Stochastic Aircraft Recovery Problem. The approach solves the problem through the rescheduling of the flight plan using delays, swaps, and cancellations. The main objective of the optimization model is to restore as much as possible the original flight schedule, minimizing the total delay and the number of cancelled flights. By applying simulation techniques, the robustness of the given solution is assessed. The proposed methodology is applied on a medium-sized scenario based on real data provided by a commercial airline. The obtained results show that the methodology described in the paper is capable of producing a feasible and robust solution for this problem.",
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"content": "The Aircraft Recovery Problem appears when external events cause disruptions in a flight schedule. Thus in order to minimize the losses caused by the externalities, aircrafts must be reallocated (rescheduled) in the best possible way. The aim of this paper is to develop a suitable methodology that combines optimization techniques with a simulation approach to tackle the so-called Stochastic Aircraft Recovery Problem. The approach solves the problem through the rescheduling of the flight plan using delays, swaps, and cancellations. The main objective of the optimization model is to restore as much as possible the original flight schedule, minimizing the total delay and the number of cancelled flights. By applying simulation techniques, the robustness of the given solution is assessed. The proposed methodology is applied on a medium-sized scenario based on real data provided by a commercial airline. The obtained results show that the methodology described in the paper is capable of producing a feasible and robust solution for this problem.",
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"fno": "5073a265",
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"abstract": "Feature detection or extraction for polygon mesh is one of the most fundamental and extensively used techniques. Applications such as mesh simplification, smoothing, parameterization, segmentation, morphing, and shape matching, etc. requires feature detection. A common technique is to identify the edge features through the dihedral angles of the two neighbouring faces adjacent to the edge. In this paper, we propose a variation of such technique that identifies feature vertices by considering the maximum normal variation of the neighbouring faces in the 1-ring neighbourhood of a vertex. By clustering and labelling the feature vertices, we may successfully identify the features of a given object as disjoint cluster of feature vertices. By means of Shortest Path or Spanning Tree with proper weighting scheme, we may further process each cluster as a feature curve that potentially enables a cut to the mesh.",
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"title": "2012 Third International Conference on Digital Manufacturing & Automation",
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"doi": "10.1109/ICDMA.2012.238",
"title": "Virtual-Joints-Based Error Compensating Algorithms for the Manipulator of Tunnel Rock-Drilling Robot",
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"abstract": "Tests showed that large position and pose errors would generate during the boring orientation of Rock-drilling robot, due to kinematic pairs clearances, flexibility, sensor errors, control errors and other algorithmic errors. As it's difficult to predict and compensate orientation errors by Denavit-Hartenberg method, the concept of virtual joints is introduced in this paper. The position and pose error is virtualized to be a rigid joint transformation, which make it simplified to formulate the transformation matrix, and the satisfactory results have been obtained.",
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"content": "Tests showed that large position and pose errors would generate during the boring orientation of Rock-drilling robot, due to kinematic pairs clearances, flexibility, sensor errors, control errors and other algorithmic errors. As it's difficult to predict and compensate orientation errors by Denavit-Hartenberg method, the concept of virtual joints is introduced in this paper. The position and pose error is virtualized to be a rigid joint transformation, which make it simplified to formulate the transformation matrix, and the satisfactory results have been obtained.",
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"normalizedAbstract": "Tests showed that large position and pose errors would generate during the boring orientation of Rock-drilling robot, due to kinematic pairs clearances, flexibility, sensor errors, control errors and other algorithmic errors. As it's difficult to predict and compensate orientation errors by Denavit-Hartenberg method, the concept of virtual joints is introduced in this paper. The position and pose error is virtualized to be a rigid joint transformation, which make it simplified to formulate the transformation matrix, and the satisfactory results have been obtained.",
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"fullName": "Xie Xihua",
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"fullName": "Zhou Liang",
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"fullName": "He Qinghua",
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"title": "Proceedings of IEEE Workshop on Biomedical Image Analysis",
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"abstract": "Describes a new concept for the description of 3D smooth surfaces: the extremal mesh. In previous works, the author has shown how to extract the extremal lines from 3D images, which are the lines where one of the two principal surface curvatures is locally extremal. He has also shown how to extract the extremal points, which are specific points where the two principal curvatures are both extremal. The extremal mesh is the graph of the surface whose vertices are the extremal points and whose edges are the extremal lines: it is invariant with respect to rigid transforms. The good topological properties of this graph are ensured with a new local geometric invariant of 3D surfaces, that the author calls the Gaussian extremality, and which allows to overcome orientation problems encountered with previous definitions of the extremal lines and points. The author also presents an algorithm to extract the extremal mesh from 3D images, and experiments with synthetic and real 3D medical images showing that this graph can be extremely precise and stable. The extremal mesh and the Gaussian extremality are new insights into the geometrical nature of 3D surfaces, with many promising consequences, some of which are listed here.<>",
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"abstract": "Segmenting images into distinct material types is a very useful capability. Most work in image segmentation addresses the case where only a single image is available. Some methods improve on this by collecting HDR or multispectral images. However, it is also possible to use the reflectance properties of the materials to obtain better results. By acquiring many images of an object under different lighting conditions we have more samples of the surfaces bidirectional reflectance distribution function (BRDF). We show that this additional information enlarges the class of material types that can be well separated by segmentation, and that properly treating the information as samples of the BRDF further increases accuracy without requiring an explicit estimation of the material BRDF.",
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"abstract": "The use of a reversible color-to-gray algorithm is an effective technique in practical applications, in terms of running cost, data quantity, security, etc. Most conventional image processing approaches cannot apply image encoding to color- embedded gray images. In this study, we propose a reversible color-to-gray method with resistance to JPEG encoding. To embed color information, the discrete cosine transformation (DCT) is employed, with good affinity to JPEG encoding. In the proposed method, first an input color image is converted to the YCbCr color space, and the DCT coefficients of the Cb and Cr components are embedded into the DCT coefficients of the Y component. Then using the JPEG quantization table, an appropriate embedding position is determined. Experiments were performed on standard color images, and the color recovery error after JPEG coding was compared and verified with conventional methods using PSNR and CIEDE2000.",
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"content": "The use of a reversible color-to-gray algorithm is an effective technique in practical applications, in terms of running cost, data quantity, security, etc. Most conventional image processing approaches cannot apply image encoding to color- embedded gray images. In this study, we propose a reversible color-to-gray method with resistance to JPEG encoding. To embed color information, the discrete cosine transformation (DCT) is employed, with good affinity to JPEG encoding. In the proposed method, first an input color image is converted to the YCbCr color space, and the DCT coefficients of the Cb and Cr components are embedded into the DCT coefficients of the Y component. Then using the JPEG quantization table, an appropriate embedding position is determined. Experiments were performed on standard color images, and the color recovery error after JPEG coding was compared and verified with conventional methods using PSNR and CIEDE2000.",
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"title": "Grayscale-Based Block Scrambling Image Encryption for Social Networking Services",
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"abstract": "This paper proposes a new block scrambling encryption scheme that enhances the security of encryption-then-compression (EtC) systems for JPEG images, which are used, for example, to securely transmit images through an untrusted channel provider. The proposed method allows the use of a smaller block size and a larger number of blocks than the conventional ones. Moreover, images encrypted using proposed scheme include less color information due to the use of grayscale even when the original image has three color channels. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. The results of an experiment in which encrypted images were uploaded to and then downloaded from Twitter and Facebook demonstrated the effectiveness of the proposed scheme for EtC systems.",
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"abstract": "In order to suppress undesirable noises in triangular meshes for quality improvement, an effective smoothing method based on curvature flow is proposed in this paper. For triangular meshes generated from planar contours, the continuity information amongst adjacent contours is utilized to determine whether a vertex represents a geometric feature to preserve or an isolated noise to smooth. Before smoothing the original mesh with an isotropic diffusion process, local adaptive refinement is performed where the sharp features are detected. In doing so, the smoothing method has both the benefits of the isotropic diffusion method, simplicity and stability, and that of the anisotropic diffusion method, feature preserving. The presented method is implemented along with several other smoothing methods, and experiments that smooth triangular meshes reconstructed from contours are performed. Experimental results demonstrate that our method can smooth triangular meshes in an effective way.",
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"abstract": "Widely known as the \"Golden Fiber\", jute is regarded as one of the most important and profitable crops in many countries including Bangladesh. Jute and jute-based commodities can bring a lot of foreign income and eventually boosts the overall economy of the country. However, many a time, jute production gets hindered due to many harmful pests and insects. Even though farmers identify and take actions against these pests following a manual procedure, it is often tedious and time-consuming. That is why it may be very beneficial to have a machine learning-based approach towards pest detection. This paper proposes a deep CNN model named \"PestDetector\" that can correctly identify 4 major types of jute pests (Field Cricket, Jute Stem Weevil, Spilosoma Obliqua, and Yellow Mite) with substantial accuracy. The work is done on a total of 2200 images separated into 3 categories: Training, Validation, and Testing. The model ultimately demonstrates 99.18% training accuracy and 99.00% validation accuracy. Additionally, the model's overall performance has been assessed using precision, recall, F1-score, and confusion matrix.",
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"abstract": "Performance degradation When running augmented reality applications in wearable platforms can be greater than expected, as desktop and mobile platforms present different levels of hardware capabilities, and consequently, different performance restrictions. This work addresses the object tracking problem using a decision model that prioritizes using the least computationally intensive algorithm whenever possible. This could be achieved by analyzing different metrics regarding image interference such as occlusion and image noise. The OTS (Object Tracking SWitcher) model allows automatic switching of different trackers to balance application's performance without compromising its tracking quality. It was validated by synthetic case studies that comprise different object classes that can be tracked, focusing on augmented reality applications that can run on restricted platforms. It was possible to reach a performance improvement of a factor of three, While keeping a minimum quality defined by a reprojection error of 10 pixels when compared to the use of only the best algorithm independent of its computational cost.",
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"content": "Performance degradation When running augmented reality applications in wearable platforms can be greater than expected, as desktop and mobile platforms present different levels of hardware capabilities, and consequently, different performance restrictions. This work addresses the object tracking problem using a decision model that prioritizes using the least computationally intensive algorithm whenever possible. This could be achieved by analyzing different metrics regarding image interference such as occlusion and image noise. The OTS (Object Tracking SWitcher) model allows automatic switching of different trackers to balance application's performance without compromising its tracking quality. It was validated by synthetic case studies that comprise different object classes that can be tracked, focusing on augmented reality applications that can run on restricted platforms. It was possible to reach a performance improvement of a factor of three, While keeping a minimum quality defined by a reprojection error of 10 pixels when compared to the use of only the best algorithm independent of its computational cost.",
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"abstract": "In this paper, we seek to extend multi-object tracking research on a relatively less explored domain, that of, underwater multi-object tracking in the wild. Multi-object fish tracking is an important task because it can provide fish monitoring systems with richer information (e.g. multiple views of the same fish) as compared to detections and it can be an invaluable input to fish behavior analysis. However, there is a lack of an annotated benchmark dataset with enough samples for this task. To circumvent the need for manual ground truth tracking annotation, we craft a synthetic dataset. Using this synthetic dataset, we train an integrated detector and tracker called SynDHN. SynDHN uses the Deep Hungarian Network (DHN), which is a differentiable approximation of the Hungarian assignment algorithm. We repurpose DHN to become the tracking component of our algorithm by performing the task of affinity estimation between detector predictions. We consider both spatial and appearance features for affinity estimation. Our results show that despite being trained on a synthetic dataset, SynDHN generalizes well to real underwater video tracking and performs better against our baseline algorithms.",
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"abstractType": "Regular",
"content": "In this paper, we seek to extend multi-object tracking research on a relatively less explored domain, that of, underwater multi-object tracking in the wild. Multi-object fish tracking is an important task because it can provide fish monitoring systems with richer information (e.g. multiple views of the same fish) as compared to detections and it can be an invaluable input to fish behavior analysis. However, there is a lack of an annotated benchmark dataset with enough samples for this task. To circumvent the need for manual ground truth tracking annotation, we craft a synthetic dataset. Using this synthetic dataset, we train an integrated detector and tracker called SynDHN. SynDHN uses the Deep Hungarian Network (DHN), which is a differentiable approximation of the Hungarian assignment algorithm. We repurpose DHN to become the tracking component of our algorithm by performing the task of affinity estimation between detector predictions. We consider both spatial and appearance features for affinity estimation. Our results show that despite being trained on a synthetic dataset, SynDHN generalizes well to real underwater video tracking and performs better against our baseline algorithms.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we seek to extend multi-object tracking research on a relatively less explored domain, that of, underwater multi-object tracking in the wild. Multi-object fish tracking is an important task because it can provide fish monitoring systems with richer information (e.g. multiple views of the same fish) as compared to detections and it can be an invaluable input to fish behavior analysis. However, there is a lack of an annotated benchmark dataset with enough samples for this task. To circumvent the need for manual ground truth tracking annotation, we craft a synthetic dataset. Using this synthetic dataset, we train an integrated detector and tracker called SynDHN. SynDHN uses the Deep Hungarian Network (DHN), which is a differentiable approximation of the Hungarian assignment algorithm. We repurpose DHN to become the tracking component of our algorithm by performing the task of affinity estimation between detector predictions. We consider both spatial and appearance features for affinity estimation. Our results show that despite being trained on a synthetic dataset, SynDHN generalizes well to real underwater video tracking and performs better against our baseline algorithms.",
"fno": "09412291",
"keywords": [
"Feature Extraction",
"Image Classification",
"Learning Artificial Intelligence",
"Object Detection",
"Object Tracking",
"Target Tracking",
"Video Signal Processing",
"Syn DHN",
"Synthetic Underwater Videos",
"Multiobject Tracking Research",
"Relatively Less Explored Domain",
"Underwater Multiobject Tracking",
"Multiobject Fish Tracking",
"Fish Monitoring Systems",
"Richer Information",
"Fish Behavior Analysis",
"Annotated Benchmark Dataset",
"Manual Ground Truth Tracking Annotation",
"Synthetic Dataset",
"Integrated Detector",
"Hungarian Assignment Algorithm",
"Tracking Component",
"Affinity Estimation",
"Underwater Video Tracking",
"Deep Hungarian Network",
"Multiobject Fish Tracker",
"Video Tracking",
"Video Sequences",
"Estimation",
"Detectors",
"Fish",
"Prediction Algorithms",
"Approximation Algorithms"
],
"authors": [
{
"affiliation": "University of the Philippines,Department of Computer Science",
"fullName": "Mygel Andrei M. Martija",
"givenName": "Mygel Andrei M.",
"surname": "Martija",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of the Philippines,Department of Computer Science",
"fullName": "Prospero C. Naval",
"givenName": "Prospero C.",
"surname": "Naval",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icpr",
"isOpenAccess": false,
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"pubDate": "2021-01-01T00:00:00",
"pubType": "proceedings",
"pages": "8841-8848",
"year": "2021",
"issn": "1051-4651",
"isbn": "978-1-7281-8808-9",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
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