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

Ron Kimmel

ISBN 10: 0444641408 / ISBN 13: 9780444641403

Published by Elsevier Science & Technology, North Holland, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9780444641403

Bibliographic Details

• Title: Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2
• Publisher: Elsevier Science & Technology, North Holland
• Publication Date: 2019
• Language: English
• ISBN 10: 0444641408
• ISBN 13: 9780444641403
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.

• 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

About the Authors

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.

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.