Unsupervised segmentation of hidden Markov fields corrupted by correlated non-Gaussian noise

Pixel labeling problem stands among the most commonly considered topics in image processing. Many statistical approaches have been developed for this purpose, particularly in the frame of hidden Markov random fields. Such models have been extended in many directions to better fit image data. Our contribution falls under such extensions and consists of introducing two new models allowing one to deal with non-Gaussian correlated noise. The first one is purely probabilistic, whereas the second one calls on Dempster-Shafer theory of evidence, both being particular triplet Markov fields. The interest of the proposed models is assessed in unsupervised segmentation of sampled and real images. While both models exhibit significant improvement with respect to classic models, the evidential model turns out to be of particular interest when the hidden label field presents fine details.