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Triplet markov trees for image segmentation

Abstract : 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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Contributor : Jean-Baptiste Courbot Connect in order to contact the contributor
Submitted on : Thursday, June 14, 2018 - 11:52:23 AM
Last modification on : Monday, October 3, 2022 - 3:44:27 AM
Long-term archiving on: : Monday, September 17, 2018 - 11:56:58 AM


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Jean-Baptiste Courbot, Emmanuel Monfrini, Vincent Mazet, Christophe Collet. Triplet markov trees for image segmentation. SSP 2018: IEEE Workshop on Statistical Signal Processing, Jun 2018, Fribourg-en-Brisgau, Germany. pp.233-237, ⟨10.1109/SSP.2018.8450841⟩. ⟨hal-01815562⟩



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