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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… Read more
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Immediately download your ebook while waiting for your print delivery. No promo code needed.
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.
Research scientists and graduate students specialising in mathematics, as well as engineers with a basic knowledge in partial differential equations and their numerical approximations.
Section One
1. Compressed Learning for Image Classification: A Deep Neural Network Approach
E. Zisselman, A. Adler and M. Elad
Section 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
RK
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