Image segmentation with a propagator Markov mesh model

We introduce the concept of a propagator function to characterize unilateral Markov fields that fulfil the positivity condition. We show that parameter estimation of a model realization corresponds to the solution of a given set of equations, which can be solved explicitly for a specific propagator function depending exclusively on identities among site labels. For a hidden model, we propose a nonsupervised, iterative scheme comprising labeling and parameter re-estimation steps. Segmentation experiments using a third order Markov mesh, together with a look-ahead algorithm for real-time label estimation, show rapid convergence of the learning algorithm and yield subjectively good results.