Abstract
The image segmentation is a fundamental tool to analyze and detect objects of interest that can be applied in many fields (medicine, satellite). In this work, we present a classical Markov model for unsupervised image segmentation: Hidden Markov Chain with Independent Noise (HMC-IN) for segmenting both gray and color images. Then, we compare five iterative algorithms EM, GEM, SEM, MCEM and ICE for estimating parameters of this model under two final bayesian decision criteria MAP and MPM according to the execution time, the convergence, the PNSR index and the rate error.
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