Markov random field modeling in image analysis
Author(s)
Bibliographic Information
Markov random field modeling in image analysis
（Computer science workbench）
Springer, 2001
- : pbk
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Note
Includes bibliographical references and index
Description and Table of Contents
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. 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.
Table of 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
by "Nielsen BookData"
