Low-Rank Models in Visual Analysis
Theories, Algorithms, and Applications

Helps users master the theory and state-of-the-art of low-rank models in visual analysis

Zhouchen Lin (Author), Hongyang Zhang (Author)

9780128127315

Paperback, published 5 June 2017

260 pages
22.9 x 15.1 x 1.7 cm, 0.43 kg

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.

1. Introduction 2. Linear Models 3. Nonlinear Models 4. Optimization Algorithms 5. Representative Applications 6. Conclusions

Subject Areas: Computer vision [UYQV]