Synopsis
The nonlinear nature of many vision tasks involves analysis over nonlinear spaces embedded in higher dimensional Euclidean spaces. Such manifolds can be studied using the theory of differential geometry. Here we develop two algorithms which can be applied over manifolds. The nonlinear mean shift algorithm is a generalization of the popular mean shift, a feature space analysis method for vector spaces. Nonlinear mean shift can be applied to any Riemannian manifold and is provably convergent to the local maxima of an appropriate kernel density. This algorithm is used for motion segmentation with different motion models and for the filtering of complex image data. The projection based M-estimator is a robust regression algorithm which does not require a user supplied estimate of the level of noise corrupting the inliers. We build on the connections between kernel density estimation and M-estimators to develop data driven rules for scale estimation. The method can be generalized to handle heteroscedastic data and subspace estimation. The results of using pbM for affine motion estimation, fundamental matrix estimation and multibody factorization are presented.
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About the Author
Dr.Subbarao has a B.Tech degree from the Indian Institute of Technology, Delhi in Electrical engineering and a PhD from Rutgers University in Computer Engineering. Peter Meer is currently a professor of Electrical and Computer Engineering at Rutgers University.
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Robust Statistics Over Riemannian Manifolds
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The nonlinear nature of many vision tasks involves analysis over nonlinear spaces embedded in higher dimensional Euclidean spaces. Such manifolds can be studied using the theory of differential geometry. Here we develop two algorithms which can be applied over manifolds. The nonlinear mean shift algorithm is a generalization of the popular mean shift, a feature space analysis method for vector spaces. Nonlinear mean shift can be applied to any Riemannian manifold and is provably convergent to the local maxima of an appropriate kernel density. This algorithm is used for motion segmentation with different motion models and for the filtering of complex image data. The projection based M-estimator is a robust regression algorithm which does not require a user supplied estimate of the level of noise corrupting the inliers. We build on the connections between kernel density estimation and M-estimators to develop data driven rules for scale estimation. The method can be generalized to handle heteroscedastic data and subspace estimation. The results of using pbM for affine motion estimation, fundamental matrix estimation and multibody factorization are presented. 132 pp. Englisch. Seller Inventory # 9783843388085
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Robust Statistics Over Riemannian Manifolds
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Subbarao RaghavDr.Subbarao has a B.Tech degree from the Indian Institute of Technology, Delhi in Electrical engineering and a PhD from Rutgers University in Computer Engineering. Peter Meer is currently a professor of Electrical an. Seller Inventory # 5468640
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Robust Statistics Over Riemannian Manifolds
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The nonlinear nature of many vision tasks involves analysis over nonlinear spaces embedded in higher dimensional Euclidean spaces. Such manifolds can be studied using the theory of differential geometry. Here we develop two algorithms which can be applied over manifolds. The nonlinear mean shift algorithm is a generalization of the popular mean shift, a feature space analysis method for vector spaces. Nonlinear mean shift can be applied to any Riemannian manifold and is provably convergent to the local maxima of an appropriate kernel density. This algorithm is used for motion segmentation with different motion models and for the filtering of complex image data. The projection based M-estimator is a robust regression algorithm which does not require a user supplied estimate of the level of noise corrupting the inliers. We build on the connections between kernel density estimation and M-estimators to develop data driven rules for scale estimation. The method can be generalized to handle heteroscedastic data and subspace estimation. The results of using pbM for affine motion estimation, fundamental matrix estimation and multibody factorization are presented.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch. Seller Inventory # 9783843388085
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Robust Statistics Over Riemannian Manifolds | Applications in Computer Vision
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Taschenbuch. Condition: Neu. Robust Statistics Over Riemannian Manifolds | Applications in Computer Vision | Raghav Subbarao (u. a.) | Taschenbuch | 132 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783843388085 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 107100897
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Robust Statistics Over Riemannian Manifolds : Applications in Computer Vision
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The nonlinear nature of many vision tasks involves analysis over nonlinear spaces embedded in higher dimensional Euclidean spaces. Such manifolds can be studied using the theory of differential geometry. Here we develop two algorithms which can be applied over manifolds. The nonlinear mean shift algorithm is a generalization of the popular mean shift, a feature space analysis method for vector spaces. Nonlinear mean shift can be applied to any Riemannian manifold and is provably convergent to the local maxima of an appropriate kernel density. This algorithm is used for motion segmentation with different motion models and for the filtering of complex image data. The projection based M-estimator is a robust regression algorithm which does not require a user supplied estimate of the level of noise corrupting the inliers. We build on the connections between kernel density estimation and M-estimators to develop data driven rules for scale estimation. The method can be generalized to handle heteroscedastic data and subspace estimation. The results of using pbM for affine motion estimation, fundamental matrix estimation and multibody factorization are presented. Seller Inventory # 9783843388085
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