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We present a novel framework for image segmentation based on the Maximum Likelihood estimator. A common hypothesis for explaining the differences among image regions is that they are generated by sampling different Likelihood Functions called models. We adopt last hypothesis and, additionally, we assume that such samples come from independent and identically distributed random variables. Thus, the probability (likelihood) that a particular model generates the observed value (at a given pixel)doi:10.1093/comjnl/bxr032 fatcat:gnp56hgp6baffetlm37qw4xf4a