Processing, Analyzing and Learning of Images, Shapes, and Forms: Volume 19, Part One provides a comprehensive survey of the contemporary developments related to the analysis and learning of images, shapes and forms. It covers mathematical models as well as fast computational techniques, and includes new chapters on Alternating diffusion: a geometric approach for sensor fusion, Shape Correspondence and Functional Maps, Geometric models for perception-based image processing, Decomposition schemes for nonconvex composite minimization: theory and applications, Low rank matrix recovery: algorithms and theory, Geometry and learning for deformation shape correspondence, and Factoring scene layout from monocular images in presence of occlusion.
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Table of Contents
Section One 1. Compressed Learning for Image Classification: A Deep Neural Network Approach E. Zisselman, A. Adler and M. EladSection Two 2. Exploiting the Structure Effectively and Efficiently in Low Rank Matrix Recovery Jian-Feng Cai and Ke Wei
Section Three 3. Partial Single- and Multi-Shape Dense Correspondence Using Functional Maps Alex Bronstein 4. Shape Correspondence and Functional Maps Maks Ovsjanikov 5. Factoring Scene Layout From Monocular Images in Presence of Occlusion Niloy J. Mitra
