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portada Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications
Type
Physical Book
Author
Publisher
Language
English
Pages
114
Format
Paperback
Dimensions
23.4 x 15.6 x 0.7 cm
Weight
0.18 kg.
ISBN13
9783319352480
Categories

Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications

Mubarak Shah (Author) · Omar Oreifej (Author) · Springer · Paperback

Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications - Oreifej, Omar ; Shah, Mubarak

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Synopsis "Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications"

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.

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