Triplet markov trees for image segmentation
Résumé
This paper introduces a triplet Markov tree model designed to minimize the block effect that may be encountered while segmenting image using Hidden Markov Tree (HMT) model-ing. We present the model specificities, the Bayesian Maximum Posterior Mode segmentation, and a parameter estimation strategy in the unsupervised context. Results on synthetic images show that the method greatly improves over HMT-based segmentation, and that the model is competitive with a hidden Markov field-based segmentation.
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