01317nas a2200109   4500008004100000245007600041210006900117260003200186300001400218520092900232856004601161       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://home.inf.u-szeged.hu/en/node/1242