An Adaptive Clustering Algorithm for Image Segmentation

The problem of segmenting images of objects with smooth surfaces is considered. Gibbs random fields are used to model the region process. The parameters of the Gibbs random field model the size and the shape of the regions. 

The intensity of each region is modeled as a slowly varying function plus white Gaussian noise. The technique being developed can be regarded as a generalization of the K-means clustering algorithm to include spatial constraints and to account for intensity variations within regions.