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Why Use Sobolev Metrics on the Space of Curves

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Cover of 'Why Use Sobolev Metrics on the Space of Curves'

Table of Contents

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    Book Overview
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    Chapter 1 Welcome to Riemannian Computing in Computer Vision
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    Chapter 2 Recursive Computation of the Fréchet Mean on Non-positively Curved Riemannian Manifolds with Applications
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    Chapter 3 Kernels on Riemannian Manifolds
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    Chapter 4 Canonical Correlation Analysis on SPD( n ) Manifolds
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    Chapter 5 Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds
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    Chapter 6 Robust Estimation for Computer Vision Using Grassmann Manifolds
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    Chapter 7 Motion Averaging in 3D Reconstruction Problems
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    Chapter 8 Lie-Theoretic Multi-Robot Localization
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    Chapter 9 Covariance Weighted Procrustes Analysis
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    Chapter 10 Elastic Shape Analysis of Functions, Curves and Trajectories
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    Chapter 11 Riemannian Computing in Computer Vision
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    Chapter 12 Elastic Shape Analysis of Surfaces and Images
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    Chapter 13 Designing a Boosted Classifier on Riemannian Manifolds
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    Chapter 14 A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision
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    Chapter 15 Domain Adaptation Using the Grassmann Manifold
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    Chapter 16 Coordinate Coding on the Riemannian Manifold of Symmetric Positive-Definite Matrices for Image Classification
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    Chapter 17 Summarization and Search Over Geometric Spaces
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