InforLorV4, Main, Merge, bibRecord, 002B88

Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

Identifieur interne : 002B88 ( Main/Merge ); précédent : 002B87; suivant : 002B89

Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

Auteurs : Nafaa Nacereddine [France] ; Salvatore Tabbone [France] ; Djemel Ziou [Canada] ; Latifa Hamami [Algérie]

Source :

RBID : Hal:inria-00542496

English descriptors

mix :
- AGGMM, EM algorithm, histogram fitting, image segmentation.

Abstract

In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using the Expectation Maximization algorithm. Histogram fitting and region uniformity measures on synthetic and real images reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.

Url:

https://hal.inria.fr/inria-00542496

DOI: 10.1109/ICPR.2010.1107

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 001016
to stream Hal, to step Curation: 001016
to stream Hal, to step Checkpoint: 002376

Links to Exploration step

Hal:inria-00542496

Le document en format XML

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<tutelles><tutelle active="#struct-92849" type="direct"><org type="institution" xml:id="struct-92849" status="VALID"><orgName>Université de Sherbrooke [Sherbrooke]</orgName>
<desc><address><addrLine>2500 boul. de l'Université, Sherbrooke, QC J1K 2R1</addrLine>
<country key="CA"></country>
</address>
<ref type="url">http://www.usherbrooke.ca/</ref>
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<country>Canada</country>
</affiliation>
</author>
<author><name sortKey="Hamami, Latifa" sort="Hamami, Latifa" uniqKey="Hamami L" first="Latifa" last="Hamami">Latifa Hamami</name>
<affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-103939" status="VALID"><orgName>Laboratoire Signal et Communications</orgName>
<orgName type="acronym">LSC</orgName>
<desc><address><addrLine>10, Av. Hassen Badi ; BP 182 ; El Harrach ; 16200 Alger</addrLine>
<country key="DZ"></country>
</address>
<ref type="url">http://www.enp.edu.dz</ref>
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<tutelles><tutelle active="#struct-27672" type="direct"><org type="institution" xml:id="struct-27672" status="VALID"><orgName>Ecole Nationale Polytechnique [Alger]</orgName>
<orgName type="acronym">ENP</orgName>
<desc><address><addrLine>10, Avenue Hassen Badi BP 182 El-Harrach Alger 16200</addrLine>
<country key="DZ"></country>
</address>
<ref type="url">http://www.enp.edu.dz/</ref>
</desc>
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</tutelle>
</tutelles>
</hal:affiliation>
<country>Algérie</country>
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<idno type="DOI">10.1109/ICPR.2010.1107</idno>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>AGGMM</term>
<term>EM algorithm</term>
<term>histogram fitting</term>
<term>image segmentation</term>
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<front><div type="abstract" xml:lang="en">In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using the Expectation Maximization algorithm. Histogram fitting and region uniformity measures on synthetic and real images reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.</div>
</front>
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Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

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