Unsupervised adaptive image segmentation

Zoltan Kato; Josiane Zerubia; Marc Berthod; Wojciech Pieczynski

Unsupervised adaptive image segmentation

Zoltan Kato
, Josiane Zerubia
, Marc Berthod
, Wojciech Pieczynski

INRIA

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

9 Citations (Scopus)

Résumé

This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.

langue originale	Anglais
Pages (de - à)	2399-2402
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

Contient cette citation

@article{8bf09fd5fbcd43c7add029fe1d65a82a,

title = "Unsupervised adaptive image segmentation",

abstract = "This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.",

author = "Zoltan Kato and Josiane Zerubia and Marc Berthod and Wojciech Pieczynski",

year = "1995",

month = jan,

day = "1",

language = "English",

volume = "4",

pages = "2399--2402",

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

issn = "0736-7791",

note = "Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) ; Conference date: 09-05-1995 Through 12-05-1995",

}

TY - JOUR

T1 - Unsupervised adaptive image segmentation

AU - Kato, Zoltan

AU - Zerubia, Josiane

AU - Berthod, Marc

AU - Pieczynski, Wojciech

PY - 1995/1/1

Y1 - 1995/1/1

N2 - This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.

AB - This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.

M3 - Conference article

AN - SCOPUS:0028996751

SN - 0736-7791

VL - 4

SP - 2399

EP - 2402

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 adaptive image segmentation

Résumé

Empreinte digitale

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