Arbres de Markov triplets pour la segmentation d'images

Abstract : This paper introduces a Triplet Markov Tree model designed to minimize the block effect that may be encountered while performing image segmentation using Hidden Markov Tree (HMT) modeling. We present the model specificities, the Bayesian MPM segmentation, and a parameter estimation strategy for the unsupervized context. Results on synthetic images show that the method greatly improves over HMT-based segmentation, and that the model performs very well for extremely faint signal segmentation (−15 to −10 dB)
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https://hal.archives-ouvertes.fr/hal-01611540
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Jean-Baptiste Courbot, Emmanuel Monfrini, Vincent Mazet, Christophe Collet. Arbres de Markov triplets pour la segmentation d'images. GRETSI 2017 : XXVIème colloque, Sep 2017, Juan Les Pins, France. pp.1 - 4. ⟨hal-01611540⟩

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