Physical description 
1 online resource (VI, 391 p.) : ill. (some col.) 
Series 
Springer English/International eBooks 2016  Full Set


Springer Engineering eBooks 2016 English+International

Contents 
Welcome to Riemannian Computing in Computer Vision  Recursive Computation of the Fŕechet Mean on NonPositively 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  LieTheoretic MultiRobot 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. 
Notes 
Includes index. 
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 structurefrommotion. 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 positivedefinite matrices (e.g. in diffusion tensor imaging), and functionspaces (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 reapplication in other contexts · Written by leading researchers in computer vision and Riemannian computing, from universities and industry. 
Other author 
Turaga, Pavan K. editor.


Srivastava, Anuj. editor.


SpringerLink issuing body.

Subject 
Computer vision  Mathematics.


Engineering.


Image processing.


Engineering mathematics.


Electronic books. 
ISBN 
9783319229577 electronic bk. 

3319229575 electronic bk. 

9783319229560 

3319229567 
Standard Number 
10.1007/9783319229577 
