Low-rank models in visual analysis : theories, algorithms, and applications
Author(s)
Bibliographic Information
Low-rank models in visual analysis : theories, algorithms, and applications
（Computer vision and pattern recognition series / series editors, Horst Bischof, Kyoung Mu Lee, Sudeep Sarkar）
Academic Press, an imprint of Elsevier, c2017
- : [pbk.]
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems.
Table of Contents
1. Introduction 2. Linear Models 3. Nonlinear Models 4. Optimization Algorithms 5. Representative Applications 6. Conclusions
by "Nielsen BookData"
