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Image Analysis, Random Fields and Dynamic Monte Carlo Methods

A Mathematical Introduction

  • Book
  • © 1995

Overview

Authors:
  1. Gerhard Winkler

    1. Mathematical Institute, Ludwig-Maximilians Universität, München, Germany

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Part of the book series: Stochastic Modelling and Applied Probability (SMAP, volume 27)

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Table of contents (21 chapters)

  1. Front Matter

    Pages I-XIV
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  2. Introduction

    1. Introduction

      • Gerhard Winkler
      Pages 1-9

  3. Bayesian Image Analysis: Introduction

    1. Front Matter

      Pages 11-11
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    2. The Bayesian Paradigm

      • Gerhard Winkler
      Pages 13-22

    3. Cleaning Dirty Pictures

      • Gerhard Winkler
      Pages 23-46

    4. Random Fields

      • Gerhard Winkler
      Pages 47-61

  4. The Gibbs Sampler and Simulated Annealing

    1. Front Matter

      Pages 63-63
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    2. Markov Chains: Limit Theorems

      • Gerhard Winkler
      Pages 65-79

    3. Sampling and Annealing

      • Gerhard Winkler
      Pages 81-98

    4. Cooling Schedules

      • Gerhard Winkler
      Pages 99-112

    5. Sampling and Annealing Revisited

      • Gerhard Winkler
      Pages 113-129

  5. More on Sampling and Annealing

    1. Front Matter

      Pages 131-131
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    2. Metropolis Algorithms

      • Gerhard Winkler
      Pages 133-154

    3. Alternative Approaches

      • Gerhard Winkler
      Pages 155-166

    4. Parallel Algorithms

      • Gerhard Winkler
      Pages 167-191

  6. Texture Analysis

    1. Front Matter

      Pages 193-193
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    2. Partitioning

      • Gerhard Winkler
      Pages 195-208

    3. Texture Models and Classification

      • Gerhard Winkler
      Pages 209-221

  7. Parameter Estimation

    1. Front Matter

      Pages 223-223
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    2. Maximum Likelihood Estimators

      • Gerhard Winkler
      Pages 225-235
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Keywords

About this book

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.

Authors and Affiliations

  • Mathematical Institute, Ludwig-Maximilians Universität, München, Germany

    Gerhard Winkler

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