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"doi": "10.1109/ISMAR.2019.00026",
"title": "Pointing and Selection Methods for Text Entry in Augmented Reality Head Mounted Displays",
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"abstract": "Augmented reality (AR) is on the rise with consumer-level head-mounted displays (HMDs) becoming available in recent years. Text entry is an essential activity for AR systems, but it is still relatively underexplored. Although it is possible to use a physical keyboard to enter text in AR systems, it is not the most optimal and ideal way because it confines the uses to a stationary position and within indoor environments. Instead, a virtual keyboard seems more suitable. Text entry via virtual keyboards requires a pointing method and a selection mechanism. Although there exist various combinations of pointing+selection mechanisms, it is not well understood how well suited each combination is to support fast text entry speed with low error rates and positive usability (regarding workload, user experience, motion sickness, and immersion). In this research, we perform an empirical study to investigate user preference and text entry performance of four pointing methods (Controller, Head, Hand, and Hybrid) in combination with two input mechanisms (Swype and Tap). Our research represents a first systematic investigation of these eight possible combinations. Our results show that Controller outperforms all the other device-free methods in both text entry performance and user experience. However, device-free pointing methods can be usable depending on task requirements and users' preferences and physical condition.",
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"content": "Augmented reality (AR) is on the rise with consumer-level head-mounted displays (HMDs) becoming available in recent years. Text entry is an essential activity for AR systems, but it is still relatively underexplored. Although it is possible to use a physical keyboard to enter text in AR systems, it is not the most optimal and ideal way because it confines the uses to a stationary position and within indoor environments. Instead, a virtual keyboard seems more suitable. Text entry via virtual keyboards requires a pointing method and a selection mechanism. Although there exist various combinations of pointing+selection mechanisms, it is not well understood how well suited each combination is to support fast text entry speed with low error rates and positive usability (regarding workload, user experience, motion sickness, and immersion). In this research, we perform an empirical study to investigate user preference and text entry performance of four pointing methods (Controller, Head, Hand, and Hybrid) in combination with two input mechanisms (Swype and Tap). Our research represents a first systematic investigation of these eight possible combinations. Our results show that Controller outperforms all the other device-free methods in both text entry performance and user experience. However, device-free pointing methods can be usable depending on task requirements and users' preferences and physical condition.",
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"affiliation": "The University of Adelaide",
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"affiliation": "Xi'an Jiaotong-Liverpool University",
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"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
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"article": {
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"doi": "10.1109/WACV45572.2020.9093524",
"title": "Periphery-Fovea Multi-Resolution Driving Model Guided by Human Attention",
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"abstract": "Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the model processes the full video frames in low resolution with large receptive fields. Its foveal vision module selects sub-regions and uses high-resolution input from those regions to improve its driving performance. We train the fovea selection module with supervision from driver gaze. We show that adding high-resolution input from predicted human driver gaze locations significantly improves the driving accuracy of the model. Our periphery-fovea multi-resolution model outperforms a uni-resolution periphery-only model that has the same amount of floating-point operations. More importantly, we demonstrate that our driving model achieves a significantly higher performance gain in pedestrian-involved critical situations than in other non-critical situations. Our code is publicly available at https://github.com/pascalxia/periphery_fovea_driving.",
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"affiliation": "University of California,Berkeley",
"fullName": "Ye Xia",
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"affiliation": "University of California,Berkeley",
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"affiliation": "University of California,Berkeley",
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"abstract": "In recent years, surface denoising has been a subject of intensive research in geometry processing. Most of the recent approaches for mesh denoising use a two-step scheme: normal filtering followed by a point updating step to match the corrected normals. In this paper we propose an adaptation of such two-step approaches for point-based surfaces, exploring three different weighting schemes for filtering normals. Moreover, we also investigate three techniques for normal estimation, analyzing the impact of each normal estimation method in the whole point-set smoothing process. Towards a quantitative analysis, in addition to conventional visual comparison, we evaluate the effectiveness of different choices of implementation using two measures, comparing our results against state-of-art point-based denoising techniques.",
"abstracts": [
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"content": "In recent years, surface denoising has been a subject of intensive research in geometry processing. Most of the recent approaches for mesh denoising use a two-step scheme: normal filtering followed by a point updating step to match the corrected normals. In this paper we propose an adaptation of such two-step approaches for point-based surfaces, exploring three different weighting schemes for filtering normals. Moreover, we also investigate three techniques for normal estimation, analyzing the impact of each normal estimation method in the whole point-set smoothing process. Towards a quantitative analysis, in addition to conventional visual comparison, we evaluate the effectiveness of different choices of implementation using two measures, comparing our results against state-of-art point-based denoising techniques.",
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"abstract": "A new design of HDB3 encoder / decoder based on FPGA is proposed to deal with the high complexity and long output delay of the encoder and no error correction function of the decoder which have been implemented so far. The encoder has the function of converting a NRZ code sequence to a HDB3 sequence and the decoder, vice versa. Meanwhile the decoder can correct the errors in the received HDB3 sequence according to a certain rule. Synthesis reports show that the encoder and decoder are both simple–structured; Simulation results show that the encoder has a shorter output delay and the decoder has a better function of error detecting and correcting which greatly improves the reliability of the system.",
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"title": "OPEN: Order-preserving Pointcloud Encoder Decoder Network for Body Shape Refinement",
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"abstract": "Image-based 3-D human body shape estimation and reconstruction have shown significant improvement by using deep neural networks. Compared with reconstructing from a single image, reconstructing 3-D human body shapes from video or image sequences requires high precision and dense correspondences between the keypoints of the reconstructed shape sequence. Existing methods cannot achieve both high accuracy and keep the dense correspondence between different shapes after reconstruction. In this paper, we propose a method named Order-preserving Point cloud Encoder-decoder Network to refine the reconstructed human body shape from SMPL with the assistance of RGB images while preserving its original dense correspondence. We further introduce using 2-D RGB images as weak supervision when 3-D labels are not available. We assess our methods on the public dataset and show improved results compared with the baseline methods.",
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"abstract": "This paper compares and analyzes the classification performance of latent vectors in the encoder-decoder model. A typical encoder-decoder model, such as an autoencoder, transforms the encoder input into a latent vector and feeds it into the decoder. In this process, the encoder-decoder model learns to produce a decoder output similar to the encoder input. We can consider that the latent vector of the encoder-decoder model is well preserved by abstracting the characteristics of the encoder input. Further, it is possible to apply to unsupervised learning if the latent vector guarantees a sufficient distance between clusters in the feature space. In this paper, the classification performance of latent vectors is analyzed as a basic study for applying latent vectors in encoder-decoder models to unsupervised and continual learning. The latent vectors obtained by the stacked autoencoder and 2 types of CNN-based autoencoder are applied to 6 kinds of classifiers including KNN and random forest. Experimental results show that the latent vector using the CNN-based autoencoder with a dense layer shows superior classification performance by up to 2% compared to the result of the stacked autoencoder. Based on the results in this paper, it is possible to extend the latent vector obtained by using a CNN-based auto-encoder with dense layer to unsupervised learning.",
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"content": "This paper compares and analyzes the classification performance of latent vectors in the encoder-decoder model. A typical encoder-decoder model, such as an autoencoder, transforms the encoder input into a latent vector and feeds it into the decoder. In this process, the encoder-decoder model learns to produce a decoder output similar to the encoder input. We can consider that the latent vector of the encoder-decoder model is well preserved by abstracting the characteristics of the encoder input. Further, it is possible to apply to unsupervised learning if the latent vector guarantees a sufficient distance between clusters in the feature space. In this paper, the classification performance of latent vectors is analyzed as a basic study for applying latent vectors in encoder-decoder models to unsupervised and continual learning. The latent vectors obtained by the stacked autoencoder and 2 types of CNN-based autoencoder are applied to 6 kinds of classifiers including KNN and random forest. Experimental results show that the latent vector using the CNN-based autoencoder with a dense layer shows superior classification performance by up to 2% compared to the result of the stacked autoencoder. Based on the results in this paper, it is possible to extend the latent vector obtained by using a CNN-based auto-encoder with dense layer to unsupervised learning.",
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"abstract": "It is essential to do the semantic segmentation task on large-scale outdoor point clouds under the demand of autonomous driving and other applications. Because there is a serious imbalance among different semantic classes, it become a challenging problem. In this paper, we propose a point-based encoder-decoder shared multi- layer perceptrons (MLPs) network with weighted focal loss for semantic segmentation of large-scale point clouds. In the proposed network, we design a residual encoding block which is composed of a relative position encoding block and two neighbor features gathering and combined pooling blocks to aggregate rich neighboring points information. To alleviate the categories imbalance problem, we adopt class-balanced sampler to get the input point cloud block per iteration and use weighted focal loss in the training process. We conducted the experiments on Toronto-3D dataset and the results show that our method achieved an overall accuracy (OA) with 95.70%, a mean intersection over union (mIoU) with 71.85% when input data only contains coordinate information, and an OA with 97.81 %, a mIoU with 81.16% when input data contains both of coordinate and color information.",
"abstracts": [
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"content": "It is essential to do the semantic segmentation task on large-scale outdoor point clouds under the demand of autonomous driving and other applications. Because there is a serious imbalance among different semantic classes, it become a challenging problem. In this paper, we propose a point-based encoder-decoder shared multi- layer perceptrons (MLPs) network with weighted focal loss for semantic segmentation of large-scale point clouds. In the proposed network, we design a residual encoding block which is composed of a relative position encoding block and two neighbor features gathering and combined pooling blocks to aggregate rich neighboring points information. To alleviate the categories imbalance problem, we adopt class-balanced sampler to get the input point cloud block per iteration and use weighted focal loss in the training process. We conducted the experiments on Toronto-3D dataset and the results show that our method achieved an overall accuracy (OA) with 95.70%, a mean intersection over union (mIoU) with 71.85% when input data only contains coordinate information, and an OA with 97.81 %, a mIoU with 81.16% when input data contains both of coordinate and color information.",
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"normalizedAbstract": "Many different approaches have been proposed for the challenging problem of visually analyzing large networks. Clustering is one of the most promising. In this paper we propose a new goal for clustering that is especially tailored to hybrid-visualization tools. Namely, that of producing both intra-cluster graphs and inter-cluster graph that are suitable for highly-readable visualizations within different representation conventions. We formalize this concept in the (X,Y)-clustering framework, where Y is the class that defines the desired topological properties of intra-cluster graphs and X is the class that defines the desired topological properties of the inter-cluster graph. By exploiting this approach hybrid-visualization tools can effectively combine different node-link and matrix-based representations, allowing the users to interactively explore the graph by expansion/contraction of clusters without loosing their mental map. As a proof of concept, we describe the system VHYXY (Visual Hybrid (X,Y)-clustering) that integrates our techniques and we present the results of case studies to the visual analysis of co-authorship networks.",
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"affiliation": "University of Perugia, Italy",
"fullName": "Walter Didimo",
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"affiliation": "University of Perugia, Italy",
"fullName": "Giuseppe Liotta",
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"affiliation": "University of Perugia, Italy",
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"affiliation": "University Roma Tre, Italy",
"fullName": "Maurizio Patrignani",
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"abstract": "Computing various global and local topological graph features is an important facet of data analysis. To do so robustly and scalably requires efficient graph algorithms that either calculate topological features exactly or approximate topological features accurately. For this reason researchers developing distributed graph analytic algorithms desire generated graph benchmarks that share the challenging characteristics of real-world graphs (small-world, scale-free, heavy-tailed degree distribution) with efficiently calculated ground truth to the desired ouput. Given two small scale-free graphs with adjacency matrices A and B, their Kronecker product graph [1] has adjacency matrix C = A ⊗ B. Such Nonstochastic Kronecker graphs are highly compressible, and many expensive global graph calculations can be computed in sublinear time, with local graph statistics computed exactly in linear time, both from a sublinear amount of storage. Therefore, this class of graphs are likely of high interest to those pursuing data analysis tasks that incorporate diverse graph-based features. Here, we extend previous results regarding local triangle statistics and demonstrate that ground truth Kronecker formulas apply to: (i) some distance-based vertex centrality metrics (vertex eccentricity and closeness centrality), (ii) internal and external edge density of communities. Moreover, we demonstrate several scaling laws apply that allow researchers to have control over various ground truth quantities.",
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"title": "2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)",
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"abstract": "In this paper, A second-order accurate, Godunov-type upwind finite volume model on non-uniform grids is developed for simulating dam-break floods. The inviscid fluxes are calculated using the HLLC (Harten-Lax-van Leer-Contact) approximate Riemann solver. A second-order spatial accuracy in space and time are achieved by two-step unsplit MUSCL-Hancock and weighted surface-depth gradient method (WSDGM). A simple but effective method is proposed for modeling wetting and drying. The friction terms are solved by a semi-implicit scheme that can effectively prevent computational instability from small depths and does not invert the direction of velocity components. The applied numerical schemes are validated using three test simulations: dam break wave on a smooth dry bed, partial dam-break simulation and application to dam-break flood in a river. The simulation results show that the proposed model is accurate, robust, and has advantages when it is applied to simulate flows with local complex topographic features or flow conditions.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, A second-order accurate, Godunov-type upwind finite volume model on non-uniform grids is developed for simulating dam-break floods. The inviscid fluxes are calculated using the HLLC (Harten-Lax-van Leer-Contact) approximate Riemann solver. A second-order spatial accuracy in space and time are achieved by two-step unsplit MUSCL-Hancock and weighted surface-depth gradient method (WSDGM). A simple but effective method is proposed for modeling wetting and drying. The friction terms are solved by a semi-implicit scheme that can effectively prevent computational instability from small depths and does not invert the direction of velocity components. The applied numerical schemes are validated using three test simulations: dam break wave on a smooth dry bed, partial dam-break simulation and application to dam-break flood in a river. The simulation results show that the proposed model is accurate, robust, and has advantages when it is applied to simulate flows with local complex topographic features or flow conditions.",
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"affiliation": "Hubei Key Lab. of Digital Valley Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China",
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"title": "Effects of Fluid-Structure Interaction on Nonlinear Seismic Response of Deep-Water Hollow Bridge Pier",
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"abstract": "This paper uses numerical method to investigate the characteristics of nonlinear seismic responses of a typical deep-water hollow bridge pier. With the bilinear moment-curvature beam elements and 3D solid elements modeling the hollow pier and potential-based fluid elements modeling the water domain, three dimensional finite element models for the typical deep-water bridge pier are built, and the numerical model is verified to be reliable. Through nonlinear time history analyses, seismic responses of the deep-water bridge pier under different water levels are studied for two cases where the hollow piers contact with outer water only and both outer and inner water. The numerical results indicates that seismic responses of the deep water hollow pier increase generally with water level, especially the shear force response of the pier bottom which is obviously enlarged by fluid-structure interaction since a low water level. For bridge piers submerged in deep water, the bending moment of the pier bottom is greater than that of dry bridge piers, which makes it easier for the pier bottom to yield. When the yielding happens, the curvature ductility demand of the bottom section is significantly amplified, and the damage index of the bridge pier is distinctly expanded. The existence of the inner water would cause further demand of the curvature ductility which may lead to aggravated damages to the deep-water piers. These findings can provide valuable guidance for future deep-water bridge design.",
"abstracts": [
{
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"content": "This paper uses numerical method to investigate the characteristics of nonlinear seismic responses of a typical deep-water hollow bridge pier. With the bilinear moment-curvature beam elements and 3D solid elements modeling the hollow pier and potential-based fluid elements modeling the water domain, three dimensional finite element models for the typical deep-water bridge pier are built, and the numerical model is verified to be reliable. Through nonlinear time history analyses, seismic responses of the deep-water bridge pier under different water levels are studied for two cases where the hollow piers contact with outer water only and both outer and inner water. The numerical results indicates that seismic responses of the deep water hollow pier increase generally with water level, especially the shear force response of the pier bottom which is obviously enlarged by fluid-structure interaction since a low water level. For bridge piers submerged in deep water, the bending moment of the pier bottom is greater than that of dry bridge piers, which makes it easier for the pier bottom to yield. When the yielding happens, the curvature ductility demand of the bottom section is significantly amplified, and the damage index of the bridge pier is distinctly expanded. The existence of the inner water would cause further demand of the curvature ductility which may lead to aggravated damages to the deep-water piers. These findings can provide valuable guidance for future deep-water bridge design.",
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"abstract": "In view of the problem that the dry ice effect simulation on the stage simulation is less, a dry ice effect simulation system including the realistic rendering module of dry ice effect and the interaction module between actors and the dry ice is proposed. For visual effect of dry ice, this paper uses a method to simulate volume fog in real time by adding visual optimization of multiple noise mixtures. Firstly, in order to solve the problem that the modeling effect of single noise fog is not natural, the Perlin noise and Worley noise are used to model the fog together to enhance the detail and flip effect of fog. Secondly, using the offset shadow algorithm to simulate the self-shadow effect, combining pixel jitter and Temporal oversampling, the soft effect of fog is increased, and the distance field shadow is added to reflect the occlusion. For dry ice interaction module, this paper uses symbol distance field to detect the collision between dry ice and actors to realize the dynamic interaction between actors and dry ice. The results show that this method can simulate the dry ice effect of stage and has the function of real-time interaction.",
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"content": "In view of the problem that the dry ice effect simulation on the stage simulation is less, a dry ice effect simulation system including the realistic rendering module of dry ice effect and the interaction module between actors and the dry ice is proposed. For visual effect of dry ice, this paper uses a method to simulate volume fog in real time by adding visual optimization of multiple noise mixtures. Firstly, in order to solve the problem that the modeling effect of single noise fog is not natural, the Perlin noise and Worley noise are used to model the fog together to enhance the detail and flip effect of fog. Secondly, using the offset shadow algorithm to simulate the self-shadow effect, combining pixel jitter and Temporal oversampling, the soft effect of fog is increased, and the distance field shadow is added to reflect the occlusion. For dry ice interaction module, this paper uses symbol distance field to detect the collision between dry ice and actors to realize the dynamic interaction between actors and dry ice. The results show that this method can simulate the dry ice effect of stage and has the function of real-time interaction.",
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"abstract": "Radial Basis Function(RBF) mesh deformation method has been widely used in CFD simulations with moving boundaries due to its high robustness and accuracy. The original implementation of the RBF mesh deformation method in OpenFOAM(a widely used CFD software) is purely serial with relatively low computational performance. To reduce the time cost of the mesh motion in large-scale simulations, this paper proposes a parallel RBF mesh deformation method with multi-greedy algorithm in OpenFOAM. The proposed multi- greedy method could reduce the control points used by the RBF interpolation on both the moving boundary and the static boundary, which is more applicable than the previous typical greedy algorithm. Based on a master-worker algorithm, the computation of the mesh deformation is highly parallelized. Tests on the benchmark of a three-dimensional moving fish show that with an error tolerance of 1e-4, the interpolation time of the internal mesh motion using our multi-greedy method is about 10.2 times faster than the original one, and with a parallelism of 132, the time cost of the whole mesh motion is greatly reduced with a speedup of 37.",
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"content": "Radial Basis Function(RBF) mesh deformation method has been widely used in CFD simulations with moving boundaries due to its high robustness and accuracy. The original implementation of the RBF mesh deformation method in OpenFOAM(a widely used CFD software) is purely serial with relatively low computational performance. To reduce the time cost of the mesh motion in large-scale simulations, this paper proposes a parallel RBF mesh deformation method with multi-greedy algorithm in OpenFOAM. The proposed multi- greedy method could reduce the control points used by the RBF interpolation on both the moving boundary and the static boundary, which is more applicable than the previous typical greedy algorithm. Based on a master-worker algorithm, the computation of the mesh deformation is highly parallelized. Tests on the benchmark of a three-dimensional moving fish show that with an error tolerance of 1e-4, the interpolation time of the internal mesh motion using our multi-greedy method is about 10.2 times faster than the original one, and with a parallelism of 132, the time cost of the whole mesh motion is greatly reduced with a speedup of 37.",
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"abstract": "In this paper, we present a novel hybrid deformation model using global mass-spring medial representation structures and local finite element model. We employ the hybrid models, by fully calculating the FEM deformation in the local operation part while only calculating the global deformation by medial representation method. To achieve the real-time requirement of realistic deformable modeling, it is necessary to use the GPU parallel computing for FEM on regional deformation details, so the major calculation work in the conjugate gradient solver for the solution matrix is moved from CPU to GPU to accelerate the effectiveness. Evaluation and experiments are also discussed.",
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"abstract": "Capturing images under high ISO mode introduces much noise. The statistics of high ISO noise is quite different from that of Gaussian noise. Therefore, this kind of noise is difficult to be removed by traditional Gaussian noise removal methods. This paper proposes a convolutional neural network (CNN) based method to jointly estimate and remove high ISO noise. There are two contributions in this paper. First, we propose a CNN based noise estimation method to estimate the pixel-wise noise level. Due to the Bayer down-sampling process in imaging, the noise variance map is characterized by Bayer patterns. Therefore, we propose packing 2 × 2 blocks in a noisy image into 4D vectors, which makes the pixels with similar noise levels be neighbors. Second, the noise variance map is correlated with the image content. Thus, we propose concatenating the estimated noise variance map with the noisy image, and feed the fused data to the denoising network. The two networks are trained together in an end-to-end fashion. Experimental results demonstrate that the proposed method outperforms state-of-the-art noise estimation and removal methods.",
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"abstract": "Noise modeling and reduction are fundamental tasks in low-level computer vision. They are particularly important for smartphone cameras relying on small sensors that exhibit visually noticeable noise. There has recently been renewed interest in using data-driven approaches to improve camera noise models via neural networks. These data-driven approaches target noise present in the raw-sensor image before it has been processed by the camera's image signal processor (ISP). Modeling noise in the RAW-rgb domain is useful for improving and testing the in-camera denoising algorithm; however, there are situations where the camera's ISP does not apply denoising or additional denoising is desired when the RAW-rgb domain image is no longer available. In such cases, the sensor noise propagates through the ISP to the final rendered image encoded in standard RGB (sRGB). The nonlinear steps on the ISP culminate in a significantly more complex noise distribution in the sRGB domain and existing raw-domain noise models are unable to capture the sRGB noise distribution. We propose a new sRGB-domain noise model based on normalizing flows that is capable of learning the complex noise distribution found in sRGB images under various ISO levels. Our normalizing flows-based approach outperforms other models by a large margin in noise modeling and synthesis tasks. We also show that image denoisers trained on noisy images synthesized with our noise model outperforms those trained with noise from baselines models.",
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"abstract": "Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images. However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects. These limitations might be harmful for subsequent scientific applications in astrophysics. Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation. In this work, we propose a two-way image translation model using GANs that exploits both paired and unpaired images in a semi-supervised manner, and introduce a noise emulating module that is able to learn and reconstruct noise characterized by high-frequency features. By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global and local properties effectively and outperforms benchmark image translation models. To our best knowledge, this work is the first attempt to apply semi-supervised methods and noise reconstruction techniques in astrophysical studies.",
"abstracts": [
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"content": "Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images. However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects. These limitations might be harmful for subsequent scientific applications in astrophysics. Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation. In this work, we propose a two-way image translation model using GANs that exploits both paired and unpaired images in a semi-supervised manner, and introduce a noise emulating module that is able to learn and reconstruct noise characterized by high-frequency features. By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global and local properties effectively and outperforms benchmark image translation models. To our best knowledge, this work is the first attempt to apply semi-supervised methods and noise reconstruction techniques in astrophysical studies.",
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"abstract": "Content Based Image Retrieval (CBIR) systems that search similar images in a large database are attracting more and more research interests recently, and have been applied to medical image characterization for expert's experience sharing. One challenging task in CBIR is how to extract features for effective image representation. Therein sparse coding technique has been proven to be an effective way to learn inherent structure features for image analysis. However, it is necessary to first vectorize the 2- or 3-dimensional spatial structure for analysis with sparse coding, and then destroy the spatial relation of nearby voxels. In this study, we propose a multilinear sparse coding method to learn features from multi-dimensional medical images. We regard high dimensional local structures as tensors and propose a K-CP (CANDECOMP/PARAFAC) algorithm to learn a tensor dictionary in an iterative way. With the learned tensor dictionary, sparse coefficients of tensor local structures are calculated by multilinear orthogonal matching pursuit (MOMP) algorithm, which is an extended multilinear version of the conventional linear OMP. The proposed multilinear sparse coding method is prospected to be more efficient and effective for inherent feature extraction compared with conventional linear methods. The proposed method is applied to a CBIR system for retrieval of focal liver lesions (FLLs) using a medical database consisting of contrast-enhanced multi-phase computer-tomography (CT) images. Experiments show that the constructed CBIR with multilinear sparse coding method can achieve promising retrieval performance.",
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"abstract": "Automatic segmentation and localization of lesions in mammogram (MG) images are challenging problems even with employing advanced methods such as deep learning (DL) methods [1]–[3]. To address these challenges, we propose to use a U-Net approach to automatically detect and segment lesions in MG images. U-Net [4] is an end-to-end convolutional neural network (CNN) based model that has achieved remarkable results in segmenting bio-medical images [5]. We modified the architecture of the U-Net model to maximize its precision such as using batch normalization, adding dropout, and data augmentations. The proposed U-Net model predicts a pixel-wise segmentation map of an input full MG image in an efficient way due to its architecture. These pixel-wise segmentation maps help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. The main challenge that most DL methods face in mammography is the need for large annotated training data-sets. To train such DL networks without over-fitting, these networks need thousands or millions of training MG images [1], [3], [5]. In contrast, U-Net is capable of learning from a relatively small training data-set compared to other DL methods [4]. We used publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and MG images from the University of Connecticut Health Center (UCHC) to train the proposed U-Net model [3]. The proposed U-Net method is trained on MG images that have mass lesions of different sizes, shapes, margins, and intensity variation around mass boundaries. All the training MG images containing suspicious areas are accompanied by associated pixel-level ground truth maps (GTMs) which indicate the background and breast lesion labels for each pixel. A total of 2066 MG images and their corresponding segmentation GTMs are used to train the proposed U-Net model. Moreover, we applied the adaptive median filter (AMF) and the contrast limited adaptive histogram equalization (CLAHE) filter to the training MG images to enhance its characteristics and improve the performance of the downstream analysis [3].We compared the efficiency of our model with those of the state-of-the-art Faster R-CNN model [6] and the region growing (RG) model [7]. We tested our proposed U-Net method using film-based and fully digitized MG images. The proposed U-Net model shows slightly better performance in detecting true segments compared to the Faster R-CNN model but outperforms it significantly in term of runtime. In addition, the proposed U-Net model gives precise segments of the lesions in the MG images. In contrast, the Faster R-CNN method gives bounding boxes surrounding the lesions. Moreover, the proposed U-Net method performs superior compared to the RG model. Data augmentation has been very effective in our experiments, resulting in an increase in the Dice similarity coefficient from 0.918 to 0.983, between the GTMs and the segmented lesions maps. Also, the proposed model yielded an Intersection over Union (IoU) of 0.974 compared to IoU of 0.966 from the state-of-the-art Faster R-CNN model. In conclusion, the performance of the proposed DL model show promises to make its practical application possible for clinical applications to assist radiologists.",
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"content": "Automatic segmentation and localization of lesions in mammogram (MG) images are challenging problems even with employing advanced methods such as deep learning (DL) methods [1]–[3]. To address these challenges, we propose to use a U-Net approach to automatically detect and segment lesions in MG images. U-Net [4] is an end-to-end convolutional neural network (CNN) based model that has achieved remarkable results in segmenting bio-medical images [5]. We modified the architecture of the U-Net model to maximize its precision such as using batch normalization, adding dropout, and data augmentations. The proposed U-Net model predicts a pixel-wise segmentation map of an input full MG image in an efficient way due to its architecture. These pixel-wise segmentation maps help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. The main challenge that most DL methods face in mammography is the need for large annotated training data-sets. To train such DL networks without over-fitting, these networks need thousands or millions of training MG images [1], [3], [5]. In contrast, U-Net is capable of learning from a relatively small training data-set compared to other DL methods [4]. We used publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and MG images from the University of Connecticut Health Center (UCHC) to train the proposed U-Net model [3]. The proposed U-Net method is trained on MG images that have mass lesions of different sizes, shapes, margins, and intensity variation around mass boundaries. All the training MG images containing suspicious areas are accompanied by associated pixel-level ground truth maps (GTMs) which indicate the background and breast lesion labels for each pixel. A total of 2066 MG images and their corresponding segmentation GTMs are used to train the proposed U-Net model. Moreover, we applied the adaptive median filter (AMF) and the contrast limited adaptive histogram equalization (CLAHE) filter to the training MG images to enhance its characteristics and improve the performance of the downstream analysis [3].We compared the efficiency of our model with those of the state-of-the-art Faster R-CNN model [6] and the region growing (RG) model [7]. We tested our proposed U-Net method using film-based and fully digitized MG images. The proposed U-Net model shows slightly better performance in detecting true segments compared to the Faster R-CNN model but outperforms it significantly in term of runtime. In addition, the proposed U-Net model gives precise segments of the lesions in the MG images. In contrast, the Faster R-CNN method gives bounding boxes surrounding the lesions. Moreover, the proposed U-Net method performs superior compared to the RG model. Data augmentation has been very effective in our experiments, resulting in an increase in the Dice similarity coefficient from 0.918 to 0.983, between the GTMs and the segmented lesions maps. Also, the proposed model yielded an Intersection over Union (IoU) of 0.974 compared to IoU of 0.966 from the state-of-the-art Faster R-CNN model. In conclusion, the performance of the proposed DL model show promises to make its practical application possible for clinical applications to assist radiologists.",
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"normalizedAbstract": "Automatic segmentation and localization of lesions in mammogram (MG) images are challenging problems even with employing advanced methods such as deep learning (DL) methods [1]–[3]. To address these challenges, we propose to use a U-Net approach to automatically detect and segment lesions in MG images. U-Net [4] is an end-to-end convolutional neural network (CNN) based model that has achieved remarkable results in segmenting bio-medical images [5]. We modified the architecture of the U-Net model to maximize its precision such as using batch normalization, adding dropout, and data augmentations. The proposed U-Net model predicts a pixel-wise segmentation map of an input full MG image in an efficient way due to its architecture. These pixel-wise segmentation maps help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. The main challenge that most DL methods face in mammography is the need for large annotated training data-sets. To train such DL networks without over-fitting, these networks need thousands or millions of training MG images [1], [3], [5]. In contrast, U-Net is capable of learning from a relatively small training data-set compared to other DL methods [4]. We used publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and MG images from the University of Connecticut Health Center (UCHC) to train the proposed U-Net model [3]. The proposed U-Net method is trained on MG images that have mass lesions of different sizes, shapes, margins, and intensity variation around mass boundaries. All the training MG images containing suspicious areas are accompanied by associated pixel-level ground truth maps (GTMs) which indicate the background and breast lesion labels for each pixel. A total of 2066 MG images and their corresponding segmentation GTMs are used to train the proposed U-Net model. Moreover, we applied the adaptive median filter (AMF) and the contrast limited adaptive histogram equalization (CLAHE) filter to the training MG images to enhance its characteristics and improve the performance of the downstream analysis [3].We compared the efficiency of our model with those of the state-of-the-art Faster R-CNN model [6] and the region growing (RG) model [7]. We tested our proposed U-Net method using film-based and fully digitized MG images. The proposed U-Net model shows slightly better performance in detecting true segments compared to the Faster R-CNN model but outperforms it significantly in term of runtime. In addition, the proposed U-Net model gives precise segments of the lesions in the MG images. In contrast, the Faster R-CNN method gives bounding boxes surrounding the lesions. Moreover, the proposed U-Net method performs superior compared to the RG model. Data augmentation has been very effective in our experiments, resulting in an increase in the Dice similarity coefficient from 0.918 to 0.983, between the GTMs and the segmented lesions maps. Also, the proposed model yielded an Intersection over Union (IoU) of 0.974 compared to IoU of 0.966 from the state-of-the-art Faster R-CNN model. In conclusion, the performance of the proposed DL model show promises to make its practical application possible for clinical applications to assist radiologists.",
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"abstract": "Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse lesions regions; 2) they ignore the strong probabilistic dependencies between target lesions dataset (e.g., enteroscopy images) and well-to-annotated source diseases dataset (e.g., gastroscope images). To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task. More specifically, a pseudo label generator is proposed to leverage seed information to generate highly-confident pseudo pixel labels by incorporating class balance and super-pixel spatial prior. It can iteratively include more hard-to-transfer samples from weakly-labeled target dataset into training set. Afterwards, dynamically-searched feature centroids for same class among different datasets are aligned by accumulating previously-learned features. Meanwhile, adversarial learning is also employed in this paper, to narrow the gap between the lesions among different datasets in output space. Finally, we build a new medical endoscopic dataset with 3659 images collected from more than 1100 volunteers. Extensive experiments on our collected dataset and several benchmark datasets validate the effectiveness of our model.",
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"content": "Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse lesions regions; 2) they ignore the strong probabilistic dependencies between target lesions dataset (e.g., enteroscopy images) and well-to-annotated source diseases dataset (e.g., gastroscope images). To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task. More specifically, a pseudo label generator is proposed to leverage seed information to generate highly-confident pseudo pixel labels by incorporating class balance and super-pixel spatial prior. It can iteratively include more hard-to-transfer samples from weakly-labeled target dataset into training set. Afterwards, dynamically-searched feature centroids for same class among different datasets are aligned by accumulating previously-learned features. Meanwhile, adversarial learning is also employed in this paper, to narrow the gap between the lesions among different datasets in output space. Finally, we build a new medical endoscopic dataset with 3659 images collected from more than 1100 volunteers. Extensive experiments on our collected dataset and several benchmark datasets validate the effectiveness of our model.",
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"normalizedAbstract": "Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse lesions regions; 2) they ignore the strong probabilistic dependencies between target lesions dataset (e.g., enteroscopy images) and well-to-annotated source diseases dataset (e.g., gastroscope images). To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task. More specifically, a pseudo label generator is proposed to leverage seed information to generate highly-confident pseudo pixel labels by incorporating class balance and super-pixel spatial prior. It can iteratively include more hard-to-transfer samples from weakly-labeled target dataset into training set. Afterwards, dynamically-searched feature centroids for same class among different datasets are aligned by accumulating previously-learned features. Meanwhile, adversarial learning is also employed in this paper, to narrow the gap between the lesions among different datasets in output space. Finally, we build a new medical endoscopic dataset with 3659 images collected from more than 1100 volunteers. Extensive experiments on our collected dataset and several benchmark datasets validate the effectiveness of our model.",
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"abstract": "SPECT bone imaging is among the main nuclear medicine functional imaging modalities, which has the potential of early diagnosis of various serious diseases such as cancers. However, the low resolution and contrast of SPECT bone images bring huge challenges to performance on segmenting hotspots or lesions in SPECT bone scan images. In this work, three different algorithms including K-means clustering method, region growth method and C- V model are introduced to segment lesions in SPECT bone scan images by fine-tuning parameters, focusing on the bone metastatic lesions area in SPECT imaging. Specifically, each of the original 256 × 1024 whole body images is cropped into a 256 × 256 thoracic region including ribs and spine, followed by the data normalization. Then, different parameters are experimentally determined for the above three algorithms, leading to three different segmentation algorithms. Last, experimental evaluation conducted on a group of real-world samples of SPECT bone scan images reveals that our methods are workable for segmenting lesions with SPECT imaging, obtaining a value of 0.7307, 0.7768 and 0.8076 for Tanimoto similarity coefficient metric by the three methods, respectively. Particularly, the C- V model based segmentation method is able to provide more assistive information for oncologists on diagnose of tumors and other related diseases.",
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"content": "The traffic congestion frequently occurs in urban transportations. In order to alleviate the traffic congestion, the vehicle information and communication system (VICS) has been developed. However, since each vehicle can obtain global information on traffic congestion using VICS, all vehicles in the congested areas tend to move to non-congested areas. As a result, the non-congested areas become congested areas. To avoid such the oscillation between the congested and non-congested areas, this paper proposes a novel method based on the vehicle ad hoc network (VANET) for alleviating traffic congestion in urban transportations. In the proposed method, since each vehicle independently collects local information on traffic congestion using VANET, traffic can be distributed from congested areas to non-congested areas. Through simulation experiments, this paper shows that the proposed method provides faster velocity and shorter trip time than VICS in the environments that traffic varies temporally and spatially, which occur in urban transportations.",
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"abstract": "In this paper, we address the security properties of automated road congestion detection systems. SCATS, SCOOT and InSync are three examples of Adaptive Traffic Control Systems (ATCSs) widely deployed today. ATCSs minimize the unused green time and reduce traffic congestion in urban areas using different methods such as induction loops and camcorders installed at intersections. The main drawback of these system is that they cannot capture incidents outside the range of these camcorders or induction loops. To overcome this hurdle, theoretical concepts for automated road congestion alarm systems including the system architecture, communication protocol, and algorithms are proposed. These concepts incorporate secure wireless vehicle-to-infrastructure (V2I) communications. The security properties of this new system are presented and then analyzed using the ProVerif protocol verification tool.",
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"abstract": "Being able to make objective Quality of Service (QoS) judgments or assessments is a challenging and crucial activity. The process of making these assessments is compounded when the environment in which the assessments have to be made are virtual; in the sense the interacting parties might not have necessarily met with each other physically. In a broad sense Quality of Service assessments could be broadly categorized into two areas, namely objective assessments and subjective assessments. In this paper, we propose a suite of metrics to carry out subjective quality assessment in a virtual environment.",
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"abstract": "In this paper, we propose to separate diffuse and specular reflection components for color images in the HSI color space. Under white illumination, pixels with the same diffuse chromaticity have the same hue. Meanwhile, specular pixels have lower saturations than the diffuse ones. Based on these properties, separating reflection components can be achieved by adjusting saturations of specular pixels to the values of diffuse-only pixels with the same diffuse chromaticity. We employ a region-growing algorithm to locate adjacent pixels with similar diffuse chromaticities. Then, the separation of reflection components is achieved by finding the optimal saturation in each connected region. The experimental results demonstrate that the proposed method is more effective to separate reflection components than the state-of-the-art methods.",
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