Robust Subspace Estimation Using Low-Rank Optimization - Theory and Applications (Paperback, Softcover reprint of the original 1st ed. 2014)

Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	The International Series in Video Computing, 12
Release date:	August 2016
First published:	2014
Authors:	Omar Oreifej • Mubarak Shah
Dimensions:	235 x 155 x 7mm (L x W x T)
Format:	Paperback
Pages:	114
Edition:	Softcover reprint of the original 1st ed. 2014
ISBN-13:	978-3-319-35248-0
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN:	3-319-35248-2
Barcode:	9783319352480

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