Riemannian Computing in Computer Vision [electronic resource] / edited by Pavan K. Turaga, Anuj Srivastava.
Turaga, Pavan K. editor.; Srivastava, Anuj. editor.
2016
TK5102.9
Available Online

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Title
Riemannian Computing in Computer Vision [electronic resource] / edited by Pavan K. Turaga, Anuj Srivastava.
ISBN
9783319229577 (electronic book)
3319229575 (electronic book)
9783319229560
3319229567
DOI
https://doi.org/10.1007/978-3-319-22957-7
Published
Cham : Springer, 2016.
Language
English
Description
1 online resource (vi, 391 pages) : illustrations.
Item Number
10.1007/978-3-319-22957-7 doi
Call Number
TK5102.9
Dewey Decimal Classification
621.382
Summary
This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours). · Illustrates Riemannian computing theory on applications in computer vision, machine learning, and robotics · Emphasis on algorithmic advances that will allow re-application in other contexts · Written by leading researchers in computer vision and Riemannian computing, from universities and industry.
Note
Includes index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Added Author
Turaga, Pavan K. editor.
Srivastava, Anuj. editor.
Available in Other Form
Print version: 9783319229560
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Online Access
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All Resources
Welcome to Riemannian Computing in Computer Vision
Recursive Computation of the Fŕechet 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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