Sparse Representation, Modeling and Learning in Visual Recognition - Theory, Algorithms and Applications (Paperback, Softcover reprint of the original 1st ed. 2015)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Advances in Computer Vision and Pattern Recognition
Release date:	October 2016
First published:	2015
Authors:	Hong Cheng
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Paperback
Pages:	257
Edition:	Softcover reprint of the original 1st ed. 2015
ISBN-13:	978-1-4471-7251-2
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	1-4471-7251-5
Barcode:	9781447172512

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