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Markov Random Field Modeling in Image Analysis

Author: Stan Z Li
Publisher: Tokyo : Springer Japan, 2001.
Series: Computer science workbench.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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. The book covers the following parts essential to the subject: introduction to  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Stan Z Li
ISBN: 9784431670445 4431670440
OCLC Number: 851366234
Description: 1 online resource (xix, 323 pages).
Contents: Foreword by Anil K. Jain --
Introduction --
Low Level MRF Models --
Discontinuities in MRFs --
Discontinuity-Adaptivity Model and Robust Estimation --
High Level MRF Models --
MRF Parameter Estimation --
Parameter Estimation in Optimal Object Recognition --
Minimization --
Local Methods --
Minimization --
Global Methods --
References --
List of Notation --
Index. The complete table of contents can be found on the Internet: http://www.springer.de.
Series Title: Computer science workbench.
Responsibility: by Stan Z. Li.

Abstract:

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. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. 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 second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. 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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