Triplet Markov Trees for Image Segmentation

摘要

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) modeling. 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 HMTbased segmentation, and that the model is competitive with a hidden Markov field-based segmentation.