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

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	2411-2414
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
Publication status	Published - 1 Jan 1995
Externally published	Yes
Event	Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA Duration: 9 May 1995 → 12 May 1995

Cite this

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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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journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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AU - Salzenstein, F.

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.

UR - https://www.scopus.com/pages/publications/0028996748

M3 - Conference article

AN - SCOPUS:0028996748

SN - 1520-6149

VL - 4

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

Abstract

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