Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2

1st Edition - October 15, 2019

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  • Editors: Ron Kimmel, Xue-Cheng Tai
  • eBook ISBN: 9780444641410
  • Hardcover ISBN: 9780444641403

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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.

Key Features

  • Covers contemporary developments relating to the analysis and learning of images, shapes and forms
  • Presents mathematical models and quick computational techniques relating to the topic
  • Provides broad coverage, with sample chapters presenting content on Alternating Diffusion and Generating Structured TV-based Priors and Associated Primal-dual Methods


Researchers, developers as well as people who want to learn the most recent and advanced developments in these fields

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

Product details

  • No. of pages: 706
  • Language: English
  • Copyright: © North Holland 2019
  • Published: October 15, 2019
  • Imprint: North Holland
  • eBook ISBN: 9780444641410
  • Hardcover ISBN: 9780444641403

About the Serial Volume Editors

Ron Kimmel

Ron Kimmel is a Professor of Computer Science at the Technion where he holds the Montreal Chair in Sciences. He held a post-doctoral position at UC Berkeley and a visiting professorship at Stanford University. He has worked in various areas of image and shape analysis in computer vision, image processing, and computer graphics. Kimmel's interest in recent years has been non-rigid shape processing and analysis, medical imaging and computational biometry, numerical optimization of problems with a geometric flavor, and applications of metric geometry, deep learning, and differential geometry. Kimmel is an IEEE Fellow for his contributions to image processing and non-rigid shape analysis. He is an author of two books, an editor of one, and an author of numerous articles. He is the founder of the Geometric Image Processing Lab. and a founder and advisor of several successful image processing and analysis companies.

Affiliations and Expertise

Technion - Israel Institute of Technology, Israel

Xue-Cheng Tai

Professor Tai Xue-Cheng is a member of the Department of Mathematics at the Hong Kong Baptist University, Hong Kong and also the University of Bergen of Norway. His research interests include Numerical partial differential equations, optimization techniques, inverse problems, and image processing. He is the winner for several prizes for his contributions to scientific computing and innovative researches for image processing. He served as organizing and program committee members for many international conferences and has been often invited for international conferences. He has served as referee and reviewers for many premier conferences and journals.

Affiliations and Expertise

Hong Kong Baptist University, Hong Kong, University of Berge, Norway

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