Unsupervised Bayesian segmentation using hidden Markovian fields

F. Salzenstein; W. Pieczynski

Unsupervised Bayesian segmentation using hidden Markovian fields

F. Salzenstein
, W. Pieczynski

CNRS SAMOVAR UMR 5157

Résultats de recherche: Contribution à un journal › Article de conférence › Revue par des pairs

Résumé

The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by Hidden Fuzzy Markov Fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called Iterative Conditional Estimation (ICE, [4]). This has been successfully applied in classical Hidden Markov Fields based segmentations ([8], [9]). The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.

langue originale	Anglais
Pages (de - à)	2411-2414
Nombre de pages	4
journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
état	Publié - 1 janv. 1995
Modification externe	Oui
Evénement	Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA Durée: 9 mai 1995 → 12 mai 1995

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title = "Unsupervised Bayesian segmentation using hidden Markovian fields",

abstract = "The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by Hidden Fuzzy Markov Fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called Iterative Conditional Estimation (ICE, [4]). This has been successfully applied in classical Hidden Markov Fields based segmentations ([8], [9]). The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.",

author = "F. Salzenstein and W. Pieczynski",

year = "1995",

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language = "English",

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AU - Pieczynski, W.

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N2 - The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by Hidden Fuzzy Markov Fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called Iterative Conditional Estimation (ICE, [4]). This has been successfully applied in classical Hidden Markov Fields based segmentations ([8], [9]). The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.

AB - The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by Hidden Fuzzy Markov Fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called Iterative Conditional Estimation (ICE, [4]). This has been successfully applied in classical Hidden Markov Fields based segmentations ([8], [9]). The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.

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M3 - Conference article

AN - SCOPUS:0028996748

SN - 1520-6149

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SP - 2411

EP - 2414

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5)

Y2 - 9 May 1995 through 12 May 1995

ER -

Unsupervised Bayesian segmentation using hidden Markovian fields

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