Oriented Triplet Markov Fields

Hidden Markov Field modeling is widely used for image segmentation. However, it sometimes lacks power to handle complex situations, e.g. correlated noise, textures or non-stationarities. This is why Pairwise, and then Triplet Markov Fields were introduced to handle in a generic fashion more complex observations. In this paper, we tackle the problem of anisotropic image modeling by introducing an Oriented Triplet Markov Field model, able to explicitly deal with oriented structures. Using oriented features in the framework of Triplet Markov Field modeling, we compare the behavior of this model towards other Markovian modeling on images containing such oriented pattern. We present experiments on synthetic data for segmentation, and application to real data from remote sensing images.