Abstract

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).

Original languageEnglish (US)
PublisherSpringer International Publishing
Number of pages391
ISBN (Print)9783319229577, 9783319229560
DOIs
StatePublished - Jan 1 2015

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Activity Recognition
Face recognition
Face Recognition
Computer Vision
Computer vision
Figure
Diffusion tensor imaging
Structure from Motion
Rotation matrix
Geometric Analysis
Symmetric Positive Definite Matrix
Computing
Geometric Approach
Object Detection
Statistical Inference
Image Analysis
Function Space
Image analysis
Tensor
Camera

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)
  • Mathematics(all)

Cite this

Riemannian computing in computer vision. / Srivastava, Anuj; Turaga, Pavan.

Springer International Publishing, 2015. 391 p.

Research output: Book/ReportBook

Srivastava A, Turaga P. Riemannian computing in computer vision. Springer International Publishing, 2015. 391 p. https://doi.org/10.1007/978-3-319-22957-7
Srivastava, Anuj ; Turaga, Pavan. / Riemannian computing in computer vision. Springer International Publishing, 2015. 391 p.
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