Hidden Markov fields and unsupervised segmentation of images

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationIAPR 1992 - 11th IAPR International Conference on Pattern Recognition
Subtitle of host publicationImage, Speech, and Signal Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-100
Number of pages5
ISBN (Electronic)0818629207
DOIs
Publication statusPublished - 1 Jan 1992
Externally publishedYes
Event11th IAPR International Conference on Pattern Recognition, IAPR 1992 - The Hague, Netherlands
Duration: 30 Aug 19921 Sept 1992

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Conference

Conference11th IAPR International Conference on Pattern Recognition, IAPR 1992
Country/TerritoryNetherlands
CityThe Hague
Period30/08/921/09/92

Fingerprint

Dive into the research topics of 'Hidden Markov fields and unsupervised segmentation of images'. Together they form a unique fingerprint.

Cite this