The Experts below are selected from a list of 546 Experts worldwide ranked by ideXlab platform
J F Peters - One of the best experts on this subject based on the ideXlab platform.
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Voronoi Region based adaptive unsupervised color image segmentation
Pattern Recognition, 2017Co-Authors: R Hettiarachchi, J F PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This paper introduces an adaptive and unsupervised approach based on Voronoi Regions to solve the color image segmentation problem. The proposed method uses a hybrid of spatial and feature space Dirichlet tessellation followed by inter-Voronoi Region proximal cluster merging to automatically find the number of clusters and cluster centroids in an image. Since, the Voronoi Regions are much smaller compared to the whole image, Voronoi Region-wise clustering improves the efficiency and accuracy of the number of clusters and cluster centroid estimation process. The proposed method was compared with four other adaptive unsupervised cluster-based image segmentation algorithms on three image segmentation evaluation benchmarks. The experimental results reported in this paper confirm that the proposed method outperforms the existing algorithms in terms of the image segmentation quality and results in much lower average execution time per image. HighlightsWe propose a hybrid of Dirichlet tessellation to automatically segment an image.Spatial Dirichlet tessellation adaptively divides an image into Voronoi Regions.Feature space Dirichlet tessellation adaptively clusters pixels in Voronoi Regions.Inter-Voronoi Region proximal cluster merging automatically finds the final clusters.
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Voronoi Region based adaptive unsupervised color image segmentation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: R Hettiarachchi, J F PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This article introduces an adaptive and unsupervised clustering approach based on Voronoi Regions, which can be applied to solve the color image segmentation problem. The proposed method performs Region splitting and merging within Voronoi Regions of the Dirichlet Tessellated image (also called a Voronoi diagram) , which improves the efficiency and the accuracy of the number of clusters and cluster centroids estimation process. Furthermore, the proposed method uses cluster centroid proximity to merge proximal clusters in order to find the final number of clusters and cluster centroids. In contrast to the existing adaptive unsupervised cluster-based image segmentation algorithms, the proposed method uses K-means clustering algorithm in place of the Fuzzy C-means algorithm to find the final segmented image. The proposed method was evaluated on three different unsupervised image segmentation evaluation benchmarks and its results were compared with two other adaptive unsupervised cluster-based image segmentation algorithms. The experimental results reported in this article confirm that the proposed method outperforms the existing algorithms in terms of the quality of image segmentation results. Also, the proposed method results in the lowest average execution time per image compared to the existing methods reported in this article.
R Hettiarachchi - One of the best experts on this subject based on the ideXlab platform.
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Voronoi Region based adaptive unsupervised color image segmentation
Pattern Recognition, 2017Co-Authors: R Hettiarachchi, J F PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This paper introduces an adaptive and unsupervised approach based on Voronoi Regions to solve the color image segmentation problem. The proposed method uses a hybrid of spatial and feature space Dirichlet tessellation followed by inter-Voronoi Region proximal cluster merging to automatically find the number of clusters and cluster centroids in an image. Since, the Voronoi Regions are much smaller compared to the whole image, Voronoi Region-wise clustering improves the efficiency and accuracy of the number of clusters and cluster centroid estimation process. The proposed method was compared with four other adaptive unsupervised cluster-based image segmentation algorithms on three image segmentation evaluation benchmarks. The experimental results reported in this paper confirm that the proposed method outperforms the existing algorithms in terms of the image segmentation quality and results in much lower average execution time per image. HighlightsWe propose a hybrid of Dirichlet tessellation to automatically segment an image.Spatial Dirichlet tessellation adaptively divides an image into Voronoi Regions.Feature space Dirichlet tessellation adaptively clusters pixels in Voronoi Regions.Inter-Voronoi Region proximal cluster merging automatically finds the final clusters.
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Voronoi Region based adaptive unsupervised color image segmentation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: R Hettiarachchi, J F PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This article introduces an adaptive and unsupervised clustering approach based on Voronoi Regions, which can be applied to solve the color image segmentation problem. The proposed method performs Region splitting and merging within Voronoi Regions of the Dirichlet Tessellated image (also called a Voronoi diagram) , which improves the efficiency and the accuracy of the number of clusters and cluster centroids estimation process. Furthermore, the proposed method uses cluster centroid proximity to merge proximal clusters in order to find the final number of clusters and cluster centroids. In contrast to the existing adaptive unsupervised cluster-based image segmentation algorithms, the proposed method uses K-means clustering algorithm in place of the Fuzzy C-means algorithm to find the final segmented image. The proposed method was evaluated on three different unsupervised image segmentation evaluation benchmarks and its results were compared with two other adaptive unsupervised cluster-based image segmentation algorithms. The experimental results reported in this article confirm that the proposed method outperforms the existing algorithms in terms of the quality of image segmentation results. Also, the proposed method results in the lowest average execution time per image compared to the existing methods reported in this article.
Dinesh Manocha - One of the best experts on this subject based on the ideXlab platform.
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DiFi: Fast 3D distance field computation using graphics hardware
Computer Graphics Forum, 2004Co-Authors: Miguel A. Otaduy, Dinesh ManochaAbstract:We present an algorithm for fast computation of discretized 3D distance elds using graphics hardware. Given a set of primitives and a distance metric, our algorithm computes the distance eld for each slice of a uniform spatial grid by rasterizing the distance functions of the primitives. We compute bounds on the spatial extent of the Voronoi Region of each primitive. These bounds are used to cull and clamp the distance functions rendered for each slice. Our algorithm is applicable to all geometric models and does not make any assumptions about connectivity or a manifold representation. We have used our algorithm to compute distance elds of large models composed of tens of thousands of primitives on high resolution grids. Moreover, we demonstrate its application to medial axis evaluation and proximity computations. As compared to earlier approaches, we are able to achieve an order of magnitude improvement in the running time.
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DiFi: Fast 3D distance field computation using graphics hardware
Computer Graphics Forum, 2004Co-Authors: Avneesh Sud, Miguel A. Otaduy, Dinesh ManochaAbstract:We present an algorithm for fast computation of discretized 3D distance fields using graphics hardware. Given a set of primitives and a distance metric, our algorithm computes the distance field for each slice of a uniform spatial grid baly rasterizing the distance functions of the primitives. We compute bounds on the spatial extent of the Voronoi Region of each primitive. These bounds are used to cull and clamp the distance functions rendered for each slice. Our algorithm is applicable to all geometric models and does not make any assumptions about connectivity or a manifold representation. We have used our algorithm to compute distance fields of large models composed of tens of thousands of primitives on high resolution grids. Moreover, we demonstrate its application to medial axis evaluation and proximity computations. As compared to earlier approaches, we are able to achieve an order of magnitude improvement in the running time.Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Distance fields, Voronoi Regions, graphics hardware, proximity computations
Laurent D Cohen - One of the best experts on this subject based on the ideXlab platform.
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fast asymmetric fronts propagation for Voronoi Region partitioning and image segmentation
Energy Minimization Methods in Computer Vision and Pattern Recognition, 2017Co-Authors: Da Chen, Laurent D CohenAbstract:In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distance map is the geodesic metric, which can govern the action of the geodesic distance level set propagation. We consider a Finsler metric with the Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow. The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes.
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EMMCVPR - Fast Asymmetric Fronts Propagation for Voronoi Region Partitioning and Image Segmentation
Lecture Notes in Computer Science, 2017Co-Authors: Da Chen, Laurent D CohenAbstract:In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distance map is the geodesic metric, which can govern the action of the geodesic distance level set propagation. We consider a Finsler metric with the Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow. The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes.
Miguel A. Otaduy - One of the best experts on this subject based on the ideXlab platform.
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DiFi: Fast 3D distance field computation using graphics hardware
Computer Graphics Forum, 2004Co-Authors: Miguel A. Otaduy, Dinesh ManochaAbstract:We present an algorithm for fast computation of discretized 3D distance elds using graphics hardware. Given a set of primitives and a distance metric, our algorithm computes the distance eld for each slice of a uniform spatial grid by rasterizing the distance functions of the primitives. We compute bounds on the spatial extent of the Voronoi Region of each primitive. These bounds are used to cull and clamp the distance functions rendered for each slice. Our algorithm is applicable to all geometric models and does not make any assumptions about connectivity or a manifold representation. We have used our algorithm to compute distance elds of large models composed of tens of thousands of primitives on high resolution grids. Moreover, we demonstrate its application to medial axis evaluation and proximity computations. As compared to earlier approaches, we are able to achieve an order of magnitude improvement in the running time.
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DiFi: Fast 3D distance field computation using graphics hardware
Computer Graphics Forum, 2004Co-Authors: Avneesh Sud, Miguel A. Otaduy, Dinesh ManochaAbstract:We present an algorithm for fast computation of discretized 3D distance fields using graphics hardware. Given a set of primitives and a distance metric, our algorithm computes the distance field for each slice of a uniform spatial grid baly rasterizing the distance functions of the primitives. We compute bounds on the spatial extent of the Voronoi Region of each primitive. These bounds are used to cull and clamp the distance functions rendered for each slice. Our algorithm is applicable to all geometric models and does not make any assumptions about connectivity or a manifold representation. We have used our algorithm to compute distance fields of large models composed of tens of thousands of primitives on high resolution grids. Moreover, we demonstrate its application to medial axis evaluation and proximity computations. As compared to earlier approaches, we are able to achieve an order of magnitude improvement in the running time.Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Distance fields, Voronoi Regions, graphics hardware, proximity computations
