00984nas a2200109   4500008004100000245004500041210004500086260003000131300001600161520065100177856004600828       1995                        eng d00aUnsupervised adaptive image segmentation0 aUnsupervised adaptive image segmentation  aPiscatawaybIEEEc1995///  a2399 - 24023 aThis paper deals with the problem of unsupervised 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 (Simulated 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 Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.  uhttps://home.inf.u-szeged.hu/en/node/124001075nas a2200109   4500008004100000245007200041210006900113260002800182300001400210520069500224856004600919       1993                        eng d00aParallel image classification using multiscale Markov random fields0 aParallel image classification using multiscale Markov random fie  aNew YorkbIEEEc1993///  a137 - 1403 aIn this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. First, we present a classical multiscale model applied to supervised image classification. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, we propose another scheme introducing a local interaction between two neighbor grids in the label pyramid. This is a way to incorporate cliques with far apart sites for a reasonable price. Finally we present the results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models.  uhttps://home.inf.u-szeged.hu/en/node/1243