Hidden Markov fields and unsupervised segmentation of images

Olivier Allagnat; Jean Marc Boucher; Dong Chen He; Wojciech Pieczynski

doi:10.1109/ICPR.1992.201936

Hidden Markov fields and unsupervised segmentation of images

Olivier Allagnat
, Jean Marc Boucher
, Dong Chen He
, Wojciech Pieczynski

ENST Bretagne
Université de Sherbrooke
CNRS SAMOVAR UMR 5157

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

This paper deals with unsupervised Bayesian segmentation of images. We introduce a new algorithm based on a recent general method of estimation in the case of incomplete data (iterative conditional estimation). The efficiency of our method is compared with a recent algorithm based on the stochastic gradient by L. Younes. We give results of numerous simulations and an application to a real radar image is also derived.

langue originale	Anglais
titre	Conference C
Sous-titre	Image, Speech and Signal Analysis
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	96-100
Nombre de pages	5
ISBN (Electronique)	0818629207
Les DOIs	https://doi.org/10.1109/ICPR.1992.201936
état	Publié - 1 janv. 1992
Modification externe	Oui
Evénement	11th IAPR International Conference on Pattern Recognition, IAPR 1992 - The Hague, Pays-Bas Durée: 30 août 1992 → 3 sept. 1992

Série de publications

Nom	Proceedings - International Conference on Pattern Recognition
Volume	3
ISSN (imprimé)	1051-4651

Une conférence

Une conférence	11th IAPR International Conference on Pattern Recognition, IAPR 1992
Pays/Territoire	Pays-Bas
La ville	The Hague
période	30/08/92 → 3/09/92

Accès au document

10.1109/ICPR.1992.201936

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Contient cette citation

@inproceedings{694cf1e3285b44508380b7a815e4f31c,

title = "Hidden Markov fields and unsupervised segmentation of images",

abstract = "This paper deals with unsupervised Bayesian segmentation of images. We introduce a new algorithm based on a recent general method of estimation in the case of incomplete data (iterative conditional estimation). The efficiency of our method is compared with a recent algorithm based on the stochastic gradient by L. Younes. We give results of numerous simulations and an application to a real radar image is also derived.",

author = "Olivier Allagnat and Boucher, \{Jean Marc\} and He, \{Dong Chen\} and Wojciech Pieczynski",

note = "Publisher Copyright: {\textcopyright} 1992 IEEE.; 11th IAPR International Conference on Pattern Recognition, IAPR 1992 ; Conference date: 30-08-1992 Through 03-09-1992",

year = "1992",

month = jan,

day = "1",

doi = "10.1109/ICPR.1992.201936",

language = "English",

series = "Proceedings - International Conference on Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "96--100",

booktitle = "Conference C",

}

Allagnat, O, Boucher, JM, He, DC & Pieczynski, W 1992, Hidden Markov fields and unsupervised segmentation of images. Dans Conference C: Image, Speech and Signal Analysis. Proceedings - International Conference on Pattern Recognition, VOL. 3, Institute of Electrical and Electronics Engineers Inc., p. 96-100, 11th IAPR International Conference on Pattern Recognition, IAPR 1992, The Hague, Pays-Bas, 30/08/92. https://doi.org/10.1109/ICPR.1992.201936

Hidden Markov fields and unsupervised segmentation of images. / Allagnat, Olivier; Boucher, Jean Marc; He, Dong Chen et al.
Conference C: Image, Speech and Signal Analysis. Institute of Electrical and Electronics Engineers Inc., 1992. p. 96-100 (Proceedings - International Conference on Pattern Recognition; Vol 3).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Hidden Markov fields and unsupervised segmentation of images

AU - Allagnat, Olivier

AU - Boucher, Jean Marc

AU - He, Dong Chen

AU - Pieczynski, Wojciech

PY - 1992/1/1

Y1 - 1992/1/1

N2 - This paper deals with unsupervised Bayesian segmentation of images. We introduce a new algorithm based on a recent general method of estimation in the case of incomplete data (iterative conditional estimation). The efficiency of our method is compared with a recent algorithm based on the stochastic gradient by L. Younes. We give results of numerous simulations and an application to a real radar image is also derived.

AB - This paper deals with unsupervised Bayesian segmentation of images. We introduce a new algorithm based on a recent general method of estimation in the case of incomplete data (iterative conditional estimation). The efficiency of our method is compared with a recent algorithm based on the stochastic gradient by L. Younes. We give results of numerous simulations and an application to a real radar image is also derived.

UR - https://www.scopus.com/pages/publications/84973287805

U2 - 10.1109/ICPR.1992.201936

DO - 10.1109/ICPR.1992.201936

M3 - Conference contribution

AN - SCOPUS:84973287805

T3 - Proceedings - International Conference on Pattern Recognition

SP - 96

EP - 100

BT - Conference C

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th IAPR International Conference on Pattern Recognition, IAPR 1992

Y2 - 30 August 1992 through 3 September 1992

ER -

Hidden Markov fields and unsupervised segmentation of images

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Contient cette citation