Comprehensive coverage over a broad range of Markov Random Field Theory
Provides the most recent advances in the field
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.
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.
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.
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 deillegalscriptive 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)
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.
This detailed book presents a comprehensive study on the use of Markov Random Fields for solving computer vision problems. Various vision models are presented, and this third edition includes the most recent advances with new and expanded sections.
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.
Introduction.- Mathematical MRF Models.- Low Level MRF Models.- High Level MRF Models.- Discontinuities in MRFs.- Discontinuity-Adaptivity Model and Robust Estimation.- MRF Parameter Estimation.- Parameter Estimation in Optimal Object Recognition.- Minimization: Local Methods.- Minimization: Global Methods.- List of Notation.- Index.
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 deillegalscriptive 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)
Inhaltsverzeichnis
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.
Klappentext
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.
Comprehensive coverage over a broad range of Markov Random Field Theory
Provides the most recent advances in the field
Includes supplementary material: sn.pub/extras
