Image Textures and Gibbs Random Fields

1. Front Matter

   Pages i-xiv

   PDF

2. Instead of introduction

   • Georgy L. Gimel’farb

   Pages 1-4

3. Texture, Structure, and Pairwise Interactions

   • Georgy L. Gimel’farb

   Pages 5-36

4. Markov and Non-Markov Gibbs Image Models

   • Georgy L. Gimel’farb

   Pages 37-62

5. Supervised MLE-Based Parameter Learning

   • Georgy L. Gimel’farb

   Pages 63-100

6. Supervised Conditional MLE-Based Learning

   • Georgy L. Gimel’farb

   Pages 101-110

7. Experiments in Simulating Natural Textures

   • Georgy L. Gimel’farb

   Pages 111-144

8. Experiments in Retrieving Natural Textures

   • Georgy L. Gimel’farb

   Pages 145-182

9. Experiments in Segmenting Natural Textures

   • Georgy L. Gimel’farb

   Pages 183-238

10. Back Matter

    Pages 239-251

    PDF

Image analysis is one of the most challenging areas in today's computer sci­ ence, and image technologies are used in a host of applications. This book concentrates on image textures and presents novel techniques for their sim­ ulation, retrieval, and segmentation using specific Gibbs random fields with multiple pairwise interaction between signals as probabilistic image models. These models and techniques were developed mainly during the previous five years (in relation to April 1999 when these words were written). While scanning these pages you may notice that, in spite of long equa­ tions, the mathematical background is extremely simple. I have tried to avoid complex abstract constructions and give explicit physical (to be spe­ cific, "image-based") explanations to all the mathematical notions involved. Therefore it is hoped that the book can be easily read both by professionals and graduate students in computer science and electrical engineering who take an interest in image analysis and synthesis. Perhaps, mathematicians studying applications of random fields may find here some less traditional, and thus controversial, views and techniques.

• Parameter
• algorithms
• computer science
• heuristics
• learning
• mathematics
• modeling
• simulation

• The University of Auckland, Auckland, New Zealand

  Georgy L. Gimel’farb

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