Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12 - Brossura

Oreifej, Omar; Shah, Mubarak

 
9783319352480: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12
Brossura
ISBN 10: 3319352482 ISBN 13: 9783319352480
Casa editrice: Springer, 2016

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2016
Lingua
Inglese
ISBN 10 
3319352482
ISBN 13 
9783319352480
Rilegatura
Copertina flessibile
Numero di pagine
120
Contatto del produttore
non disponibile
Persona responsabile
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Germania

Altre edizioni note dello stesso titolo

9783319041834: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12

Edizione in evidenza

ISBN 10:  3319041835 ISBN 13:  9783319041834
Casa editrice: Springer-Nature New York Inc, 2014
Rilegato