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Other titles in the Advances in Pattern Recognition series:

Markov Random Field Modeling in Image Analysis (Advances in Pattern Recognition)

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Markov Random Field Modeling in Image Analysis (Advances in Pattern Recognition) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

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.

Synopsis:

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.

Table of Contents

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.

Product Details

ISBN:
9781848002784
Author:
Li, Stan Z.
Publisher:
Springer
Subject:
Computer Science
Subject:
Computer Graphics - Image Processing
Subject:
Computer Vision
Subject:
Image processing
Subject:
Personal Computers-General
Subject:
Bayesian modeling
Subject:
Computer vison
Subject:
Image analysis.
Subject:
Markov Random Field
Subject:
OPTIMIZATION
Subject:
Pattern recognition.
Subject:
Image Processing and Computer Vision
Subject:
Mathematics of Computing
Copyright:
Edition Description:
3rd ed. 2009
Series:
Advances in Computer Vision and Pattern Recognition
Publication Date:
20090310
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
384
Dimensions:
235 x 155 mm 1570 gr

Related Subjects

Computers and Internet » Computers Reference » General
Computers and Internet » Graphics » Image Processing
Computers and Internet » Personal Computers » General
Health and Self-Help » Health and Medicine » Medical Specialties

Markov Random Field Modeling in Image Analysis (Advances in Pattern Recognition) New Hardcover
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$140.25 In Stock
Product details 384 pages Springer - English 9781848002784 Reviews:
"Synopsis" by , 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.
"Synopsis" by , 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.
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