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Riemannian computing in computer vis...
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Riemannian computing in computer vision[electronic resource] /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
杜威分類號:
621.382
書名/作者:
Riemannian computing in computer vision/ edited by Pavan K. Turaga, Anuj Srivastava.
其他作者:
Turaga, Pavan K.
出版者:
Cham : : Springer International Publishing :, 2016.
面頁冊數:
vi, 391 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:
Springer eBooks
標題:
Computer vision - Mathematics.
標題:
Engineering.
標題:
Image processing.
標題:
Engineering mathematics.
標題:
Signal, Image and Speech Processing.
標題:
Image Processing and Computer Vision.
標題:
Applications of Mathematics.
ISBN:
9783319229577
ISBN:
9783319229560
內容註:
Welcome to Riemannian Computing in Computer Vision -- Recursive Computation of the Fr´echet Mean on Non-Positively Curved Riemannian Manifolds with Applications -- Kernels on Riemannian Manifolds -- Canonical Correlation Analysis on SPD(n) manifolds -- Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds -- Robust Estimation for Computer Vision using Grassmann Manifolds -- Motion Averaging in 3D Reconstruction Problems -- Lie-Theoretic Multi-Robot Localization -- CovarianceWeighted Procrustes Analysis -- Elastic Shape Analysis of Functions, Curves and Trajectories -- Why Use Sobolev Metrics on the Space of Curves -- Elastic Shape Analysis of Surfaces and Images -- Designing a Boosted Classifier on Riemannian Manifolds -- A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision -- Domain Adaptation Using the Grassmann Manifold -- Coordinate Coding on the Riemannian Manifold of Symmetric Positive Definite Matrices for Image Classification -- Summarization and Search over Geometric Spaces.
電子資源:
http://dx.doi.org/10.1007/978-3-319-22957-7
Riemannian computing in computer vision[electronic resource] /
Riemannian computing in computer vision
[electronic resource] /edited by Pavan K. Turaga, Anuj Srivastava. - Cham :Springer International Publishing :2016. - vi, 391 p. :ill. (some col.), digital ;24 cm.
Welcome to Riemannian Computing in Computer Vision -- Recursive Computation of the Fr´echet Mean on Non-Positively Curved Riemannian Manifolds with Applications -- Kernels on Riemannian Manifolds -- Canonical Correlation Analysis on SPD(n) manifolds -- Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds -- Robust Estimation for Computer Vision using Grassmann Manifolds -- Motion Averaging in 3D Reconstruction Problems -- Lie-Theoretic Multi-Robot Localization -- CovarianceWeighted Procrustes Analysis -- Elastic Shape Analysis of Functions, Curves and Trajectories -- Why Use Sobolev Metrics on the Space of Curves -- Elastic Shape Analysis of Surfaces and Images -- Designing a Boosted Classifier on Riemannian Manifolds -- A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision -- Domain Adaptation Using the Grassmann Manifold -- Coordinate Coding on the Riemannian Manifold of Symmetric Positive Definite Matrices for Image Classification -- Summarization and Search over Geometric Spaces.
ISBN: 9783319229577
Standard No.: 10.1007/978-3-319-22957-7doiSubjects--Topical Terms:
461283
Computer vision
--Mathematics.
LC Class. No.: TK5102.9
Dewey Class. No.: 621.382
Riemannian computing in computer vision[electronic resource] /
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Welcome to Riemannian Computing in Computer Vision -- Recursive Computation of the Fr´echet Mean on Non-Positively Curved Riemannian Manifolds with Applications -- Kernels on Riemannian Manifolds -- Canonical Correlation Analysis on SPD(n) manifolds -- Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds -- Robust Estimation for Computer Vision using Grassmann Manifolds -- Motion Averaging in 3D Reconstruction Problems -- Lie-Theoretic Multi-Robot Localization -- CovarianceWeighted Procrustes Analysis -- Elastic Shape Analysis of Functions, Curves and Trajectories -- Why Use Sobolev Metrics on the Space of Curves -- Elastic Shape Analysis of Surfaces and Images -- Designing a Boosted Classifier on Riemannian Manifolds -- A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision -- Domain Adaptation Using the Grassmann Manifold -- Coordinate Coding on the Riemannian Manifold of Symmetric Positive Definite Matrices for Image Classification -- Summarization and Search over Geometric Spaces.
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