Riemannian Computing in Computer Vision

Anuj Srivastava

ISBN 10: 3319360957 / ISBN 13: 9783319360959

Published by Springer International Publishing Aug 2016, 2016

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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). 400 pp. Englisch.

Seller Inventory # 9783319360959

Bibliographic Details

• Title: Riemannian Computing in Computer Vision
• Publisher: Springer International Publishing Aug 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3319360957
• ISBN 13: 9783319360959
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 22D millimeters 235 millimeters width by 155 millimeters height by 22 millimeters depth
• Weight: 604 grams

About this title

Synopsis

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 Author

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