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Riemannian computing in computer vision

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 approa...

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Detalles Bibliográficos
Autores principales: Turaga, Pavan, Srivastava, Anuj
Lenguaje:eng
Publicado: Springer 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-22957-7
http://cds.cern.ch/record/2112763
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author Turaga, Pavan
Srivastava, Anuj
author_facet Turaga, Pavan
Srivastava, Anuj
author_sort Turaga, Pavan
collection CERN
description 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.
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spelling cern-21127632021-04-21T20:01:18Zdoi:10.1007/978-3-319-22957-7http://cds.cern.ch/record/2112763engTuraga, PavanSrivastava, AnujRiemannian computing in computer visionEngineeringThis 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.Springeroai:cds.cern.ch:21127632016
spellingShingle Engineering
Turaga, Pavan
Srivastava, Anuj
Riemannian computing in computer vision
title Riemannian computing in computer vision
title_full Riemannian computing in computer vision
title_fullStr Riemannian computing in computer vision
title_full_unstemmed Riemannian computing in computer vision
title_short Riemannian computing in computer vision
title_sort riemannian computing in computer vision
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-22957-7
http://cds.cern.ch/record/2112763
work_keys_str_mv AT turagapavan riemanniancomputingincomputervision
AT srivastavaanuj riemanniancomputingincomputervision