Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications (The International Series in Video Computing, 12)

Oreifej, Omar, Shah, Mubarak

ISBN 10: 3319352482 / ISBN 13: 9783319352480

Editore: Springer, 2016

Riguardo questo articolo

Like New. Codice articolo ERICA80033193524826

Dati bibliografici

Titolo: Robust Subspace Estimation Using Low-Rank ...

Casa editrice: Springer

Data di pubblicazione: 2016

Legatura: Paperback

Condizione: Like New

Tipologia articolo: book

Descrizione articolo

Riassunto:

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.