Low-Rank Models in Visual Analysis
Theories, Algorithms, and Applications
Nonfiction, Science & Nature, Technology, Engineering, Computers, Application Software, General Computing
| Author: | Zhouchen Lin, Hongyang Zhang | ISBN: | 9780128127322 |
| Publisher: | Elsevier Science | Publication: | June 6, 2017 |
| Imprint: | Academic Press | Language: | English |
| Author: | Zhouchen Lin, Hongyang Zhang |
| ISBN: | 9780128127322 |
| Publisher: | Elsevier Science |
| Publication: | June 6, 2017 |
| Imprint: | Academic Press |
| Language: | English |
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.
- Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications
- Provides a full and clear explanation of the theory behind the models
- Includes detailed proofs in the appendices
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.
- Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications
- Provides a full and clear explanation of the theory behind the models
- Includes detailed proofs in the appendices
