Titlebook: High-Dimensional and Low-Quality Visual Information Processing; From Structured Sens Yue Deng Book 2015 Springer-Verlag Berlin Heidelberg 2

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Yue Deng, electromagnetics, mathematical finance, biomedical enginee.The present volume is comprised of contributions solicited from invitees to conferences held at the University of Houston, Jyv?skyl? University, and Xi’an Jiaotong University honoring the 70th birthday of Professor Roland Glowinski. Althou

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Yue DengXi’an Jiaotong University honoring the 70th birthday of Professor Roland Glowinski. Although scientists convened on three different continents, the Editors prefer to view the meetings as single event. The three locales signify the fact Roland has friends, collaborators and admirers across the globe.

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Introduction,n processing and indicate the irresistible trend of their marriage in this big data era. After introducing the low-quality properties in visual data, it will be apparent why computational methods provide an effective way to cope with these defects in visual information processing. Then, four differe

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Sparse Structure for Visual Information Sensing: Theory and Algorithms,f compressive sensing, we will discuss the problem of low-rank structure learning (LRSL) from sparse outliers. Different from traditional approaches, which directly utilize convex norms to measure the sparseness, our method introduces more reasonable non-convex measurements to enhance the sparsity i

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Sparse Structure for Visual Signal Sensing: Application in 3D Reconstruction,m using a low rank structure learning model proposed in last chapter. With this framework, we construct the initial incomplete matrix from the observed point clouds by all cameras, with the invisible points by any camera denoted as unknown entries. The observed points corresponding to the same objec