Sparse Representation, Modeling and Learning in Visual Recognition

Theory, Algorithms and Applications

By:

Hong Cheng

Format:

Paperback / softback

Series:

Advances in Computer Vision and Pattern Recognition

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Description

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.

Author Biography:

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.

Release date NZ

October 9th, 2016

Author

Hong Cheng

Series

Advances in Computer Vision and Pattern Recognition

Pages

257

Edition

Softcover reprint of the original 1st ed. 2015

Audience

Professional & Vocational

Illustrations

73 Illustrations, black and white; XIV, 257 p. 73 illus.

Dimensions

155x235x14

ISBN-13

9781447172512

Product ID

26132582

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