Robust Subspace Estimation Using Low-Rank Optimization

Name: Robust Subspace Estimation Using Low-Rank Optimization
Brand: Springer International Publishing AG
SKU: 9783319352480
Price: 59.1 EUR
Availability: InStock

Theory and Applications

Автор: Omar Oreifej, Mubarak Shah

Език:

Корици: С меки корици

Издател: Springer International Publishing AG

Наличност: Външен склад в ограничено количество

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59.10 € 115.59 лв

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

Информация за книгата

Автор

Omar Oreifej, Mubarak Shah

Език

Английски език

Корици

Книга - С меки корици

Издадена

2016

страници

114

EAN

9783319352480

ISBN

3319352482

Enbook ID

13633515

Издател

Springer International Publishing AG

Теглоt

1942

Размери

155 x 235 x 7

Пълно описание

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

Информация за книгата

Пълно описание

Може също да ви хареса

Клиенти, които купиха тази книга, купиха също