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

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, supported by an explanation of the underlying theory. 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 modelling, image alignment and rectification, motion segmentation, image segmentation, image saliency detection. With this book the reader will learn.... Which Low-rank models are highly useful in practice(both linear and nonlinear models)How to solve low-rank models efficiently, where both convex and nonconvex algorithms, as well as randomized models, are introducedHow to apply low-rank models to real problems, with applications in video denoising, background modelling, image alignment and rectification, image segmentation, motion segmentation, image saliency detection, partial-duplicate image retrieval, image tag completion and refinement, etc.How to analyze representative low-rank models theoretically Self- contained up-to-date introduction: presents underlying theory, algorithms, state-of-the-art and current applicationsFull and clear explanation of the theory behind the modelsDetailed proofs are given in the appendices INDICE: 1. Introduction 2. Linear Models 3. Nonlinear Models 4. Optimization Algorithms 5. Representative Applications 6. Conclusions

• ISBN: 978-0-12-812731-5
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 225
• Fecha Publicación: 26/06/2017
• Nº Volúmenes: 1
• Idioma: Inglés