Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications (Advances in Computer Vision and Pattern Recognition)

Cheng, Hong

ISBN 10: 1447172515 / ISBN 13: 9781447172512

Edité par Springer, 2016

A propos de cet article

pp. 272. N° de réf. du vendeur 26378353733

Détails bibliographiques

Titre : Sparse Representation, Modeling and Learning...

Éditeur : Springer

Date d'édition : 2016

Reliure : Couverture souple

Etat : New

A propos de ce titre

Synopsis :

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

À propos de l?auteur:

Dr. Hong Cheng is Professor in the School of Automation Engineering, and Deputy Executive Director of the Center for Robotics at the University of Electronic Science and Technology of China. His other publications include the Springer book Autonomous Intelligent Vehicles.