00964nas a2200133   4500008004100000020001400041245005300055210005200108260001200160300001600172490000600188520058900194856004700783       1995                        eng d  a1057-714900aDPA: a deterministic approach to the MAP problem0 aDPA a deterministic approach to the MAP problem  c1995///  a1312 - 13140 v43 aDeterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.  uhttps://tisat.inf.u-szeged.hu/en/node/123900985nas a2200109   4500008004100000245004500041210004500086260003000131300001600161520065100177856004700828       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://tisat.inf.u-szeged.hu/en/node/124001620nas a2200109   4500008004100000245008400041210006900125260003000194300001400224520122500238856004701463       1995                        eng d00aUnsupervised parallel image classification using a hierarchical Markovian model0 aUnsupervised parallel image classification using a hierarchical   aPiscatawaybIEEEc1995///  a169 - 1743 aThis paper deals with the problem of unsupervised classification 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 only observable image. Our approach consists of extending a recent iterative method of estimation, called Iterative Conditional Estimation (ICE) to a hierarchical markovian model. The idea resembles the Estimation-Maximization (EM) algorithm as we recursively look at the Maximum a Posteriori (MAP) estimate of the label field given the estimated parameters then we look at the Maximum Likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. We propose unsupervised image classification algorithms using a hierarchical model. The only parameter supposed to be known is the number of regions, all the other parameters are estimated. The presented algorithms have been implemented on a Connection Machine CM200. Comparative tests have been done on noisy synthetic and real images (remote sensing).  uhttps://tisat.inf.u-szeged.hu/en/node/124101318nas a2200109   4500008004100000245007600041210006900117260003200186300001400218520092900232856004701161       1993                        eng d00aMultiscale Markov random field models for parallel image classification0 aMultiscale Markov random field models for parallel image classif  aLos AlamitosbIEEEc1993///  a253 - 2573 aIn this paper, we are interested in multiscale Markov Random Field (MRF) models. It is well known that multigrid methods can improve significantly the convergence rate and the quality of the final results of iterative relaxation techniques. Herein, we propose a new hierarchical model, which consists of a label pyramid and a whole observation field. The parameters of the coarse grid can be derived by simple computation from the finest grid. In the label pyramid, we have introduced a new local interaction between two neighbor grids. This model gives a relaxation algorithm which can be run in parallel on the entire pyramid. On the other hand, the new model allows to propagate local interactions more efficiently giving estimates closer to the global optimum for deterministic as well as for stochastic relaxation schemes. It can also be seen as a way to incorporate cliques with far apart sites for a reasonable price.  uhttps://tisat.inf.u-szeged.hu/en/node/124201076nas a2200109   4500008004100000245007200041210006900113260002800182300001400210520069500224856004700919       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://tisat.inf.u-szeged.hu/en/node/1243