Abstract

Markov random field models have become useful in several areas of image processing. The success of MRFs can be attributed to the fact that they give rise to good, flexible, stochastic image models. The goal of image modeling is to find a suitable representation of the intensity distribution of a given image. What is adequate often depends on the task at hand and MRF image models have been versatile enough to be applied in the areas of image and texture synthesis, image compression, restoration, texture classification, and surface reconstruction. Tomographic reconstruction, image and texture segmentation. Our aim is to highlight the central ideas of this field using illustrative examples and provide pointers to the many applications.