Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability, 27)

Winkler, Gerhard

ISBN 10: 3540442138 / ISBN 13: 9783540442134

Published by Springer, 2002

About this Item

very clean 2nd edition hardcover. no marks. clean text. solid binding. very light cover edge wear. ISBN matches listing. Fast service with confirmation, no international or priority orders over 4lbs.

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Bibliographic Details

• Title: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability, 27)
• Publisher: Springer
• Publication Date: 2002
• Language: English
• ISBN 10: 3540442138
• ISBN 13: 9783540442134
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: Very Good

About this title

Synopsis

This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.
The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added.

From the Back Cover

The book is mainly concerned with the mathematical foundations of Bayesian image analysis and its algorithms. This amounts to the study of Markov random fields and dynamic Monte Carlo algorithms like sampling, simulated annealing and stochastic gradient algorithms. The approach is introductory and elemenatry: given basic concepts from linear algebra and real analysis it is self-contained. No previous knowledge from image analysis is required. Knowledge of elementary probability theory and statistics is certainly beneficial but not absolutely necessary. The necessary background from imaging is sketched and illustrated by a number of concrete applications like restoration, texture segmentation and motion analysis.