Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2 - 1st Edition - ISBN: 9780444641403

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20

1st Edition

Serial Volume Editors: Ron Kimmel Xue-Cheng Tai
Hardcover ISBN: 9780444641403
Imprint: North Holland
Published Date: 1st October 2019
Page Count: 525
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Table of Contents

1. Alternating diffusion: a geometric approach for sensor fusion
Ronen Talmon
2. Generating structured TV-based priors and associated primal-dual methods
Michael Hintermueller
3. Graph-based optimization approaches for machine learning, uncertainty quantification and networks
Andrea L. Bertozzi
4. Extrinsic shape analysis from boundary representations
Justin Solomon
5. Efficient numerical methods for gradient flows and phase-field models
Jie Shen
6. Recent Advances in Denoising of Manifold-Valued Images
Gabriele Steidl
7. Optimal Registration of Images, Surfaces and Shapes
Chen Ke
8. Finite Difference Methods for Approximating Total Variation Flow
Joachim Weickert
9. Metric invariants of curves and surfaces
Dan Raviv
10. Using Geodesics to find the global minimum of different kinds of active contours for Segmentation
Laurent D. Cohen
11. Geometric PDEs on manifolds represented as point clouds and applications
Hongkai Zhao
12. Operator-based representations for geometry processing
Mirela Ben Chen
13. Variational time discretization of Riemannian splines
Martin Rumpf
14. Survey of geometry inspired variational segmentation: interface model, curvature terms and fast computation
Sung ha Kang and Xue-Cheng Tai
15. Recent developments for fast operator splitting algorithms for variational models
Xue-Cheng Tai
16. Fast discrete algorithms for image segmentations
Yuri Boykov
17. Metric Registration of Curves and Surfaces using Optimal Control
Laurent Younes
18. Active Contour Methods on Arbitrary Graphs Based on Partial Differential Equations
Petros Maragos
19. Lagrangian Methods for Composite Optimization
Marc Teboulle and Shoham Sabach
20. Tightening continuous relaxations for MAP inference in discrete MRFs: A survey
Nikos Paragios and Hariprasad Kannan


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.

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

Readership

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


Details

No. of pages:
525
Language:
English
Copyright:
© North Holland 2019
Published:
Imprint:
North Holland
Hardcover ISBN:
9780444641403

Ratings and Reviews


About the Serial Volume Editors

Ron Kimmel Serial Volume Editor

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 Serial Volume Editor

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