Overview

This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER, D. and S. GEMAN, B.R. HUNT and many others, and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as 'Bayesian Image Analysis'. There has been considerable and still growing interest in prior models and, in particular, in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such 'classical' applications like restoration, edge detection, texture discrimination, motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW, U. GRENANDER and D.M. KEENAN (1987), (1990) strongly support this belief.

Full Product Details

Author:   Gerhard Winkler
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of the original 1st ed. 1995
Volume:   27
Dimensions:   Width: 15.50cm , Height: 1.80cm , Length: 23.50cm
Weight:   0.517kg
ISBN:  

9783642975240

ISBN 10:   3642975240
Pages:   324
Publication Date:   19 January 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Replaced By:   9783642629112
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

I. Bayesian Image Analysis: Introduction.- 1. The Bayesian Paradigm.- 2. Cleaning Dirty Pictures.- 3. Random Fields.- II. The Gibbs Sampler and Simulated Annealing.- 4. Markov Chains: Limit Theorems.- 5. Sampling and Annealing.- 6. Cooling Schedules.- 7. Sampling and Annealing Revisited.- III. More on Sampling and Annealing.- 8. Metropolis Algorithms.- 9. Alternative Approaches.- 10. Parallel Algorithms.- IV. Texture Analysis.- 11. Partitioning.- 12. Texture Models and Classification.- V. Parameter Estimation.- 13. Maximum Likelihood Estimators.- 14. Spacial ML Estimation.- VI. Supplement.- 15. A Glance at Neural Networks.- 16. Mixed Applications.- VII. Appendix.- A. Simulation of Random Variables.- B. The Perron-Frobenius Theorem.- C. Concave Functions.- D. A Global Convergence Theorem for Descent Algorithms.- References.

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