The Experts below are selected from a list of 32580 Experts worldwide ranked by ideXlab platform
Uma Mudenagudi - One of the best experts on this subject based on the ideXlab platform.
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example based 3d inpainting of point clouds using Metric Tensor and christoffel symbols
Machine Vision Applications, 2018Co-Authors: Shankar Setty, Uma MudenagudiAbstract:In this paper, we address the problem of 3D inpainting using example-based methods for point cloud data. 3D inpainting is a process of filling holes or missing regions in the reconstructed 3D models. Typically inpainting methods addressed in the literature fill missing regions due to occlusions or inaccurate scanning of 3D models. However, we focus on scenarios involving naturally existing damaged models which are partly broken or incomplete in artifacts at cultural heritage sites. We propose two example-based inpainting techniques, namely region of interest (ROI)-based and patch-based methods, to inpaint the missing regions of the damaged model. For both the methods, we represent the 3D model as a set of Riemannian manifolds in Euclidean space, to capture the inherent geometry using Metric Tensor and Christoffel symbols as geoMetric features and decompose into basic shape (such as spherical, conical and cylindrical) regions using decomposition algorithm derived from supervised learning. In ROI-based method, instead of using single similar example for inpainting, we select the most relevant regions that best-fit the missing region from the set of basic shape regions derived from n similar examples. And in patch-based method, we not only select the most relevant regions but cluster the regions into a set of patches. The best corresponding patches that match the missing region to be inpainted are considered to be the most relevant best-fit patches that cover the complete missing region. We demonstrate the performance of proposed inpainting methods on cultural heritage artifacts with varying complexities and sizes for both synthetically generated holes and real missing regions.
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ACCV Workshops (3) - Metric Tensor and Christoffel Symbols Based 3D Object Categorization
Computer Vision - ACCV 2014 Workshops, 2015Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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3d object super resolution using Metric Tensor and christoffel symbols
Indian Conference on Computer Vision Graphics and Image Processing, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
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Metric Tensor and christoffel symbols based 3d object categorization
Asian Conference on Computer Vision, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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Metric Tensor and christoffel symbols based 3d object categorization
International Conference on Computer Graphics and Interactive Techniques, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Shetty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model the 3D object as a 2D Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features capture the local and global geometry of 3D objects by exploiting the positional dependence of the features. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieve comparable results.
Syed Altaf Ganihar - One of the best experts on this subject based on the ideXlab platform.
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ACCV Workshops (3) - Metric Tensor and Christoffel Symbols Based 3D Object Categorization
Computer Vision - ACCV 2014 Workshops, 2015Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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3d object super resolution using Metric Tensor and christoffel symbols
Indian Conference on Computer Vision Graphics and Image Processing, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
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Metric Tensor and christoffel symbols based 3d object categorization
Asian Conference on Computer Vision, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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Metric Tensor and christoffel symbols based 3d object categorization
International Conference on Computer Graphics and Interactive Techniques, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Shetty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model the 3D object as a 2D Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features capture the local and global geometry of 3D objects by exploiting the positional dependence of the features. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieve comparable results.
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ICVGIP - 3D Object Super Resolution using Metric Tensor and Christoffel Symbols
Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '14, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
Janne Pesonen - One of the best experts on this subject based on the ideXlab platform.
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eckart frame vibration rotation hamiltonians contravariant Metric Tensor
Journal of Chemical Physics, 2014Co-Authors: Janne PesonenAbstract:Eckart frame is a unique embedding in the theory of molecular vibrations and rotations. It is defined by the condition that the Coriolis coupling of the reference structure of the molecule is zero for every choice of the shape coordinates. It is far from trivial to set up Eckart kinetic energy operators (KEOs), when the shape of the molecule is described by curvilinear coordinates. In order to obtain the KEO, one needs to set up the corresponding contravariant Metric Tensor. Here, I derive explicitly the Eckart frame rotational measuring vectors. Their inner products with themselves give the rotational elements, and their inner products with the vibrational measuring vectors (which, in the absence of constraints, are the mass-weighted gradients of the shape coordinates) give the Coriolis elements of the contravariant Metric Tensor. The vibrational elements are given as the inner products of the vibrational measuring vectors with themselves, and these elements do not depend on the choice of the body-frame....
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constrained molecular vibration rotation hamiltonians contravariant Metric Tensor
Journal of Chemical Physics, 2013Co-Authors: Janne PesonenAbstract:Here, I present a practical recipe for obtaining contravariant vibration-rotation Metric Tensors, and thus the kinetic energy operators, when some degrees of freedom are constrained rigidly. An element of the contravariant Metric Tensor is obtained as a sum of dot products of contravariant measuring vectors, which are obtained from their unconstrained counterparts by adding a frozen mode correction. The present method applies in principle for any choice of shape coordinates and a body-frame for which the contravariant measuring vectors can be evaluated. In contrast to the existing methods, the present method does not involve evaluation of covariant Metric Tensors, matrix inversions, chain rules of derivation, or numerical differentiation. It is applied in the sequel paper [L. Partanen, J. Pesonen, E. Sjoholm, and L. Halonen, J. Chem. Phys. 139, 144311 (2013)] to study the effects of several different approximations to the kinetic energy operator, when the two large-amplitude OH-torsional motions in H2SO4 ...
Shreyas Joshi - One of the best experts on this subject based on the ideXlab platform.
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ACCV Workshops (3) - Metric Tensor and Christoffel Symbols Based 3D Object Categorization
Computer Vision - ACCV 2014 Workshops, 2015Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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3d object super resolution using Metric Tensor and christoffel symbols
Indian Conference on Computer Vision Graphics and Image Processing, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
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Metric Tensor and christoffel symbols based 3d object categorization
Asian Conference on Computer Vision, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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Metric Tensor and christoffel symbols based 3d object categorization
International Conference on Computer Graphics and Interactive Techniques, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Shetty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model the 3D object as a 2D Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features capture the local and global geometry of 3D objects by exploiting the positional dependence of the features. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieve comparable results.
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ICVGIP - 3D Object Super Resolution using Metric Tensor and Christoffel Symbols
Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '14, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
Shankar Setty - One of the best experts on this subject based on the ideXlab platform.
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example based 3d inpainting of point clouds using Metric Tensor and christoffel symbols
Machine Vision Applications, 2018Co-Authors: Shankar Setty, Uma MudenagudiAbstract:In this paper, we address the problem of 3D inpainting using example-based methods for point cloud data. 3D inpainting is a process of filling holes or missing regions in the reconstructed 3D models. Typically inpainting methods addressed in the literature fill missing regions due to occlusions or inaccurate scanning of 3D models. However, we focus on scenarios involving naturally existing damaged models which are partly broken or incomplete in artifacts at cultural heritage sites. We propose two example-based inpainting techniques, namely region of interest (ROI)-based and patch-based methods, to inpaint the missing regions of the damaged model. For both the methods, we represent the 3D model as a set of Riemannian manifolds in Euclidean space, to capture the inherent geometry using Metric Tensor and Christoffel symbols as geoMetric features and decompose into basic shape (such as spherical, conical and cylindrical) regions using decomposition algorithm derived from supervised learning. In ROI-based method, instead of using single similar example for inpainting, we select the most relevant regions that best-fit the missing region from the set of basic shape regions derived from n similar examples. And in patch-based method, we not only select the most relevant regions but cluster the regions into a set of patches. The best corresponding patches that match the missing region to be inpainted are considered to be the most relevant best-fit patches that cover the complete missing region. We demonstrate the performance of proposed inpainting methods on cultural heritage artifacts with varying complexities and sizes for both synthetically generated holes and real missing regions.
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ACCV Workshops (3) - Metric Tensor and Christoffel Symbols Based 3D Object Categorization
Computer Vision - ACCV 2014 Workshops, 2015Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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3d object super resolution using Metric Tensor and christoffel symbols
Indian Conference on Computer Vision Graphics and Image Processing, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
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Metric Tensor and christoffel symbols based 3d object categorization
Asian Conference on Computer Vision, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we propose to address the problem of 3D object categorization. We model 3D object as a piecewise smooth Riemannian manifold and propose Metric Tensor and Christoffel symbols as a novel set of features. The proposed set of features captures the local and global geometry of 3D objects by exploiting the uniqueness and compatibility of the features. The Metric Tensor represents a geoMetrical signature of the 3D object in a Riemannian manifold. To capture global geometry we propose to use combination of Metric Tensor and Christoffel symbols, as Christoffel symbols measure the deviations in the Metric Tensor. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieved comparable results.
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ICVGIP - 3D Object Super Resolution using Metric Tensor and Christoffel Symbols
Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '14, 2014Co-Authors: Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, Uma MudenagudiAbstract:In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian Metric Tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using Metric Tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.