Low-Rank Models In Visual Analysis: Theories, Algorithms, And Applications (Computer Vision And Pattern Recognition)
Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications (Computer Vision and Pattern Recognition)
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
About the Author
Zhouchen Lin received the Ph.D. degree in applied mathematics from Peking University in 2000. He is currently a Professor at Key Laboratory of Machine Perception
(MOE), School of Electronics Engineering and Computer Science, Peking University. His research areas include computer vision, image processing, machine learning, pattern recognition, and numerical optimization. He is an area chair of CVPR...