TY - GEN
T1 - Triplet Markov Trees for Image Segmentation
AU - Courbot, Jean Baptiste
AU - Monfrini, Emmanuel
AU - Mazet, Vincent
AU - Collet, Christophe
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - 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.
AB - 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.
KW - Image Segmentation
KW - Triplet Markov Tree
KW - Unsupervised segmentation
U2 - 10.1109/SSP.2018.8450841
DO - 10.1109/SSP.2018.8450841
M3 - Conference contribution
AN - SCOPUS:85053816358
SN - 9781538615706
T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018
SP - 628
EP - 632
BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018
Y2 - 10 June 2018 through 13 June 2018
ER -