Markovian Segmentation of Non-stationary Data Corrupted by Non-stationary Noise

Hidden Markov models have been widely applied for data processing, mainly for image segmentation. However, when applied to non-stationary data, the results obtained may be poor due to mismatch between the models and reality. To this, such models have been extended to triplet Markov models which exhibit better performances while keeping similar computational complexity. In this paper, we introduce two novel models, based on triplet chain and field, able to handle simultaneously noise and classes non-stationarity, through a pairwise markovian auxiliary process. The performances of our models were assessed against regular hidden Markov models on simulated and synthetic images segmentation. The results obtained confirm the interest of the models proposed.