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.
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01815562
Contributor : Jean-Baptiste Courbot <>
Submitted on : Thursday, June 14, 2018 - 11:52:23 AM
Last modification on : Thursday, October 17, 2019 - 12:36:54 PM
Long-term archiving on : Monday, September 17, 2018 - 11:56:58 AM

File

ssp_final.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

88

Files downloads

76