Spatial Sampling for Image Segmentation
We present a framework for image segmentation based on the ML estimator. A common hypothesis for explaining the differences among image regions is that they are generated by sampling different Likelihood Functions. We adopt last hypothesis and, additionally, we assume that such samples are i.i.d. Thus, the probability of a model generates the observed pixel value is estimated by computing the likelihood of the sample composed with the surrounding pixels.
KeywordsImage Segmentation Spatial Sampling Neighborhood Selection Bayesian Regularization Markov Random Field Modeling
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