Handbook of Image and Video Processing, 2nd Edition

4.3

Random Field Models

P. Fieguth,     University of Waterloo, Ontario

J. Zhang,     University of Wisconsin, Milwaukee

1 Introduction.

2 Random Fields - Overview.

2.1 Markov Random Fields

2.2 Gauss-Markov Random Fields

2.3 Gibbs Random Fields

3 Multiscale Random Fields.

3.1 Discrete-State Models

3.2 Continuous-State Models

3.3 Examples

4 A Nonlinear/Non-Gaussian Model: the Gaussian Mixture.

4.1 The Gaussian Mixture Model

4.2 Some Applications

Acknowledgment.

References.

1 Introduction

Random fluctuations in intensity, color, texture, object boundary or shape can be seen in most real-world images, as illustrated in Fig. 1. The causes for these fluctuations are diverse and complex, often due to factors such as non-uniform lighting, ...