Riemannian Computing in Computer Vision

  • Pavan K. Turaga
  • Anuj Srivastava

Table of contents

  1. Front Matter
    Pages i-vi
  2. Anuj Srivastava, Pavan K. Turaga
    Pages 1-18
  3. Statistical Computing on Manifolds

    1. Front Matter
      Pages 19-19
    2. Guang Cheng, Jeffrey Ho, Hesamoddin Salehian, Baba C. Vemuri
      Pages 21-43
    3. Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann
      Pages 45-67
    4. Hyunwoo J. Kim, Nagesh Adluru, Barbara B. Bendlin, Sterling C. Johnson, Baba C. Vemuri, Vikas Singh
      Pages 69-100
  4. Color, Motion, and Stereo

    1. Front Matter
      Pages 123-123
    2. Saket Anand, Sushil Mittal, Peter Meer
      Pages 125-144
    3. Venu Madhav Govindu
      Pages 145-164
    4. Xiao Li, Gregory S. Chirikjian
      Pages 165-186
  5. Shapes, Surfaces, and Trajectories

    1. Front Matter
      Pages 187-187
    2. Christopher J. Brignell, Ian L. Dryden, William J. Browne
      Pages 189-209
    3. Shantanu H. Joshi, Jingyong Su, Zhengwu Zhang, Boulbaba Ben Amor
      Pages 211-231
    4. Martin Bauer, Martins Bruveris, Peter W. Michor
      Pages 233-255
    5. Sebastian Kurtek, Ian H. Jermyn, Qian Xie, Eric Klassen, Hamid Laga
      Pages 257-277
  6. Objects, Humans, and Activity

    1. Front Matter
      Pages 279-279
    2. Fatih Porikli, Oncel Tuzel, Peter Meer
      Pages 281-301
    3. David A. Shaw, Rama Chellappa
      Pages 325-343

About this book


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


Diffusion Tensor Imaging Grassmann Manifold Inferences on Nonlinear Manifolds Linear Dynamical Models Riemannian Computing Riemannian Computing in Computer Vision Riemannian Geometry Tensor Manifold

Editors and affiliations

  • Pavan K. Turaga
    • 1
  • Anuj Srivastava
    • 2
  1. 1.Arizona State UniversityTempeUSA
  2. 2.Florida State UniversityTallahasseeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-22957-7
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-22956-0
  • Online ISBN 978-3-319-22957-7
  • About this book