A class of discrete multiresolution random fields and its application to image segmentation

The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures.

The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.