Riemannian Computing in Computer Vision
(Sprache: Englisch)
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...
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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).
Inhaltsverzeichnis zu „Riemannian Computing in Computer Vision “
Riemannian analysis of closed curves and applications in medical imaging.- Human Activity analysis using manifold models of linear dynamical systems.- Pedestrian detection and tracking on tensor manifolds.- Face Recognition using subspaces and Grassmann manifold.- Domain Adaptation using parallel transports on Grassmann manifold.- Kernels on Riemannian Manifolds.- Sparse coding on manifolds and applications to diffusion tensor imaging.- Robust statistics on manifolds and applications to chromatic filtering.- Lighting insensitive face recognition.- Lie-algebraic averaging in Structure from motion problems.- Lie-algebraic methods for Robotic localization problems.- Clustering on Gauss maps for real-time interactive media.- Medical Shape Analysis.- Bayesian Shape Analysis in Images.- Tensor Data Analysis in Medical Imaging.- Trajectories on Manifolds for Activity Recognition.- Data approximation and summarization algorithms on manifolds
Autoren-Porträt
Pavan Turaga is an Assistant Professor at Arizona State University Anuj Srivastava is a Professor at Florida State University
Bibliographische Angaben
- 2016, Softcover reprint of the original 1st ed. 2016, 391 Seiten, 66 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Pavan K. Turaga, Anuj Srivastava
- Verlag: Springer, Berlin
- ISBN-10: 3319360957
- ISBN-13: 9783319360959
Sprache:
Englisch
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