Unsupervised Adaptive Image Segmentation

This paper deals with the problem of unsupervlsed Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Sim- ulated Annealing, ICM, etc... ). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estima- tion (ICE): applied to a monogrid Markovlan image segmentation model. The method has been tested on synthetic and real satellite images