Riemannian Computing in Computer Vision
Springer International Publishing (Verlag)
978-3-319-36095-9 (ISBN)
Pavan Turaga is an Assistant Professor at Arizona State University Anuj Srivastava is a Professor at Florida State University
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.
Erscheinungsdatum | 19.08.2017 |
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Zusatzinfo | VI, 391 p. 88 illus., 66 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 611 g |
Themenwelt | Technik ► Elektrotechnik / Energietechnik |
Schlagworte | diffusion tensor imaging • Grassmann manifold • Inferences on Nonlinear Manifolds • Linear Dynamical Models • Riemannian Computing • Riemannian Computing in Computer Vision • Riemannian Geometry • Tensor Manifold |
ISBN-10 | 3-319-36095-7 / 3319360957 |
ISBN-13 | 978-3-319-36095-9 / 9783319360959 |
Zustand | Neuware |
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