Robust Subspace Estimation Using Low-Rank Optimization

Name: Robust Subspace Estimation Using Low-Rank Optimization
Brand: Springer International Publishing AG
SKU: 9783319352480
Price: 54.15 EUR
Availability: InStock

Theory and Applications

Autor: Omar Oreifej, Mubarak Shah

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer International Publishing AG

Dostupnosť: Skladom u dodávateľa v malom množstve

Odosielame za 13-18 dní

54.15 €

Various fundamental applications in computer vision and machine learning require finding the basis o...

Informácie o knihe

Autor

Omar Oreifej, Mubarak Shah

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2016

Stránok

114

EAN

9783319352480

ISBN

3319352482

Enbook ID

13633515

Vydavateľ

Springer International Publishing AG

Hmotnosť

1942

Rozmery

155 x 235 x 7

Kompletný popis

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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Robust Subspace Estimation Using Low-Rank Optimization

Theory and Applications

Informácie o knihe

Kompletný popis

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Zákazníci, ktorí si kúpili túto knihu, kúpili tiež