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The paper treats a multiresolution hidden Markov model MHMM for classifying images. Each image is represented by feature vectors at several resolutions, which are statistically dependent as modeled by the underlying state process, a multiscale Markov mesh. Unknowns in the model are estimated by maximum likelihood, in particular by employing the EM algorithm. An image is classi ed by nding the optimal set of states with maximum a posteriori probability MAP. States are then mapped into classes.doi:10.1109/18.857794 fatcat:3g2bstyf55dclhkvnc6gs6p6ni