### 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 language | English (US) |
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Publisher | Springer International Publishing |

Number of pages | 391 |

ISBN (Print) | 9783319229577, 9783319229560 |

DOIs | |

State | Published - Jan 1 2015 |

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### ASJC Scopus subject areas

- Engineering(all)
- Computer Science(all)
- Mathematics(all)

### Cite this

*Riemannian computing in computer vision*. Springer International Publishing. https://doi.org/10.1007/978-3-319-22957-7

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

Research output: Book/Report › Book

*Riemannian computing in computer vision*. Springer International Publishing. https://doi.org/10.1007/978-3-319-22957-7

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AU - Srivastava, Anuj

AU - Turaga, Pavan

PY - 2015/1/1

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N2 - 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).

AB - 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).

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M3 - Book

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PB - Springer International Publishing

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