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Riemannian Computing in Computer Vision

Editors: Turaga, Pavan K., Srivastava, Anuj (Eds.)

  • 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
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eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-22957-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.99
price for USA in USD
  • ISBN 978-3-319-22956-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $149.99
price for USA in USD
  • ISBN 978-3-319-36095-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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).

About the authors

Pavan Turaga is an Assistant Professor at Arizona State University Anuj Srivastava is a Professor at Florida State University

Table of contents (17 chapters)

  • Welcome to Riemannian Computing in Computer Vision

    Srivastava, Anuj (et al.)

    Pages 1-18

  • Recursive Computation of the Fréchet Mean on Non-positively Curved Riemannian Manifolds with Applications

    Cheng, Guang (et al.)

    Pages 21-43

  • Kernels on Riemannian Manifolds

    Jayasumana, Sadeep (et al.)

    Pages 45-67

  • Canonical Correlation Analysis on SPD(n) Manifolds

    Kim, Hyunwoo J. (et al.)

    Pages 69-100

  • Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds

    Fletcher, P. Thomas (et al.)

    Pages 101-121

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-22957-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.99
price for USA in USD
  • ISBN 978-3-319-22956-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $149.99
price for USA in USD
  • ISBN 978-3-319-36095-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Riemannian Computing in Computer Vision
Editors
  • Pavan K. Turaga
  • Anuj Srivastava
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-22957-7
DOI
10.1007/978-3-319-22957-7
Hardcover ISBN
978-3-319-22956-0
Softcover ISBN
978-3-319-36095-9
Edition Number
1
Number of Pages
VI, 391
Number of Illustrations
22 b/w illustrations, 66 illustrations in colour
Topics