Low-Rank and Sparse Modeling for Visual Analysis (Paperback, Softcover reprint of the original 1st ed. 2014)

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	October 2016
First published:	2014
Editors:	Yun Fu
Dimensions:	235 x 155 x 13mm (L x W x T)
Format:	Paperback
Pages:	236
Edition:	Softcover reprint of the original 1st ed. 2014
ISBN-13:	978-3-319-35567-2
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Computing & IT > Applications of computing > Image processing > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	3-319-35567-8
Barcode:	9783319355672

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