Processing, analyzing and learning of images, shapes, and forms
Author(s)
Bibliographic Information
Processing, analyzing and learning of images, shapes, and forms
（Handbook of numerical analysis / general editors, P.G. Ciarlet, J.L. Lions, v. 19-20）
North-Holland, c2018-
- pt. 1
- pt. 2
Available at / 6 libraries
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Science and Technology Library, Kyushu University
pt. 1P 018/PROC/2-1130012018015072,
pt. 2CIAR/10/5-20130012019019300 -
Kobe University Library for Science and Technology
pt. 1418-1-118//19030201801059,
pt. 2418-1-118//20030201900720 -
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Note
Includes bibliographical references and index
Description and Table of Contents
- Volume
-
pt. 2 ISBN 9780444641403
Description
Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising of Manifold-Valued Images, Optimal Registration of Images, Surfaces and Shapes, and much more.
Table of Contents
1. Diffusion operators for multimodal data analysis Tal Shnitzer, Roy R. Lederman, Gi-Ren Liu, Ronen Talmon and Hau-tieng Wu 2. Intrinsic and extrinsic operators for shape analysis Yu Wang and Justin Solomon 3. Operator-based representations of discrete tangent vector fields Mirela Ben-Chen and Omri Azencot 4. Active contour methods on arbitrary graphs based on partial differential equations Christos Sakaridis, Nikos Kolotouros, Kimon Drakopoulos and Petros Maragos 5. Fast operator-splitting algorithms for variational imaging models: Some recent developments Roland Glowinski, Shousheng Luo and Xue-Cheng Tai 6. From active contours to minimal geodesic paths: New solutions to active contours problems by Eikonal equations Da Chen and Laurent D. Cohen 7. Computable invariants for curves and surfaces Oshri Halimi, Dan Raviv, Yonathan Aflalo and Ron Kimmel 8. Solving PDEs on manifolds represented as point clouds and applications Rongjie Lai and Hongkai Zhao 9. Tightening continuous relaxations for MAP inference in discrete MRFs: A survey Hariprasad Kannan, Nikos Komodakis and Nikos Paragios 10. Lagrangian methods for composite optimization Shoham Sabach and Marc Teboulle 11. Generating structured nonsmooth priors and associated primal-dual methods Michael Hintermuller and Kostas Papafitsoros 12. Graph-based optimization approaches for machine learning, uncertainty quantification and networks Andrea L. Bertozzi and Ekaterina Merkurjev 13. Survey of fast algorithms for Euler's elastica-based image segmentation Sung Ha Kang, Xuecheng Tai and Wei Zhu 14. Recent advances in denoising of manifold-valued images R. Bergmann, F. Laus, J. Persch and G. Steidl 15. Image and surface registration Ke Chen, Lok Ming Lui and Jan Modersitzki 16. Metric registration of curves and surfaces using optimal control Martin Bauer, Nicolas Charon and Laurent Younes 17. Efficient and accurate structure preserving schemes for complex nonlinear systems Jie Shen
- Volume
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pt. 1 ISBN 9780444642059
Description
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.
Table of Contents
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
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