Markov Random Field Modeling in Image Analysis by Stan Z. Li (English) Paperback

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Markov Random Field Modeling in Image Analysis by Stan Z. Li

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation.

FORMAT
Paperback LANGUAGE
English CONDITION
Brand New Publisher Description

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

Back Cover

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimization. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Conditional Random Fields; Discriminative Random Fields; Total Variation (TV) Models; Spatio-temporal Models; MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts; and Face Detection and Recognition. Features: * Focuses on applying Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain * Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice, and MRFs on relational graphs derived from images * Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation * Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting * Studies discontinuities, an important issue in the application of MRFs to image analysis * Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition * Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses relating to these areas.

Table of Contents

Mathematical MRF Models.- Low-Level MRF Models.- High-Level MRF Models.- Discontinuities in MRF#x0027;s.- MRF Model with Robust Statistics.- MRF Parameter Estimation.- Parameter Estimation in Optimal Object Recognition.- Minimization – Local Methods.- Minimization – Global Methods.

Review

From the reviews of the third edition: "Prof. Li's book ! provides a comprehensive introduction to the area of MRF in general and to its applications in image processing in specific. ! is very well written with a plethora of references for the reader that wants to delve further into specific areas. ! In conclusion, this book is very thorough, both in a mathematic and a descriptive manner. Anyone interested in image processing and its applications ! can benefit from the variety of provided examples and its wide range of references." (Apostolos Georgakis, IAPR Newsletter, Vol. 31 (4), October, 2009) "This book elegantly and effectively elaborates on MRF theory and related topics. Each chapter includes the problem definition, related mathematical formulation and method explanations, and very useful examples. ! This is an excellent book on MRF theory for image analysis. Researchers and graduate students will find this book very useful for understanding the theory clearly." (Fatih Kurugollu, ACM Computing Reviews, November, 2009)

Long Description

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: * Discriminative Random Fields (DRF) * Strong Random Fields (SRF) * Spatial-Temporal Models * Total Variation Models * Learning MRF for Classification (motivation + DRF) * Relation to Graphic Models * Graph Cuts * Belief Propagation Features: * Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain * Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation * Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting * Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images * Examines the problems of parameter estimation and function optimization * Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.

Review Quote

From the reviews of the third edition:"Prof. Li's book … provides a comprehensive introduction to the area of MRF in general and to its applications in image processing in specific. … is very well written with a plethora of references for the reader that wants to delve further into specific areas. … In conclusion, this book is very thorough, both in a mathematic and a descriptive manner. Anyone interested in image processing and its applications … can benefit from the variety of provided examples and its wide range of references." (Apostolos Georgakis, IAPR Newsletter, Vol. 31 (4), October, 2009)"This book elegantly and effectively elaborates on MRF theory and related topics. Each chapter includes the problem definition, related mathematical formulation and method explanations, and very useful examples. … This is an excellent book on MRF theory for image analysis. Researchers and graduate students will find this book very useful for understanding the theory clearly." (Fatih Kurugollu, ACM Computing Reviews, November, 2009)

Feature

Comprehensive coverage over a broad range of Markov Random Field Theory Provides the most recent advances in the field

Details ISBN1849967679 Author Stan Z. Li Publisher Springer London Ltd Year 2010 Edition 3rd ISBN-10 1849967679 ISBN-13 9781849967679 Format Paperback Publication Date 2010-10-21 Imprint Springer London Ltd Place of Publication England Country of Publication United Kingdom Replaces 9784431703099 Affiliation Chinese Academy of Sciences Series Advances in Computer Vision and Pattern Recognition Short Title MARKOV RANDOM FIELD MODELING I Language English Media Book Residence US DEWEY 004 Pages 362 AU Release Date 2010-10-21 NZ Release Date 2010-10-21 UK Release Date 2010-10-21 Alternative 9781848002784 Audience Professional & Vocational Edition Description Third Edition 2009 Country of Origin NL Product Class Description Computing: Professional & Programming Illustrations 111 Illustrations, black and white
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