Utilisation de modèles markoviens 2D pour la segmentation d'images de documents
Identifieur interne : 000A05 ( France/Analysis ); précédent : 000A04; suivant : 000A06Utilisation de modèles markoviens 2D pour la segmentation d'images de documents
Auteurs : Stéphane Nicolas [France] ; Julien Dardenne [France] ; Thierry Paquet [France] ; Laurent Heutte [France]Source :
- Revue I3 - Information Interaction Intelligence [ 1630-649X ] ; 2008.
Descripteurs français
- mix :
Abstract
Nous nous intéressons dans ces travaux à l'analyse d'images par le biais de modèles markoviens génératifs et discriminants : les champs de Markov et les champs aléatoires conditionnels. Pour les champs conditionnels nous présentons une implantation 2D de ces modèles principalement utilisés jusqu'à présent pour l'analyse de données monodimensionnelles. Le modèle que nous proposons repose sur une approche de type combinaison de classifieurs discriminants. Nous illustrons et comparons les capacités de cette approche par rapport aux modèles de Champs de Markov Cachés au travers d'un exemple d'application à l'analyse de la structure de documents manuscrits complexes et dégradés : les manuscrits de Flaubert.
Url:
Affiliations:
- France
- Auvergne-Rhône-Alpes, Haute-Normandie, Rhône-Alpes, Région Normandie
- Le Havre, Lyon, Rouen
- Université Claude Bernard Lyon 1, Université de Lyon, Université de Rouen, Université du Havre
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 000786
- to stream Hal, to step Curation: 000786
- to stream Hal, to step Checkpoint: 000600
- to stream Main, to step Merge: 000E75
- to stream Main, to step Curation: 000D29
- to stream Main, to step Exploration: 000D29
- to stream France, to step Extraction: 000A05
Links to Exploration step
Hal:hal-00509164Le document en format XML
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Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université de Rouen</orgName></affiliation></author><author><name sortKey="Heutte, Laurent" sort="Heutte, Laurent" uniqKey="Heutte L" first="Laurent" last="Heutte">Laurent Heutte</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-389520" status="INCOMING"> <orgName>DOCAPP</orgName> <desc> <address> <country key="FR"></country> </address> </desc> <listRelation> <relation active="#struct-23832" type="direct"></relation> <relation active="#struct-300317" type="indirect"></relation> <relation name="EA4108" active="#struct-300318" type="indirect"></relation> <relation active="#struct-301288" type="indirect"></relation> <relation active="#struct-301232" type="indirect"></relation> </listRelation><tutelles><tutelle active="#struct-23832" type="direct"><org type="laboratory" xml:id="struct-23832" status="VALID"><orgName>Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</orgName><orgName type="acronym">LITIS</orgName><desc><address><addrLine>Avenue de l'Université UFR des Sciences et Techniques 76800 Saint-Etienne du Rouvray</addrLine><country key="FR"></country></address><ref type="url">http://www.litislab.eu</ref></desc><listRelation><relation active="#struct-300317" type="direct"></relation><relation name="EA4108" active="#struct-300318" type="direct"></relation><relation active="#struct-301288" type="direct"></relation><relation active="#struct-301232" type="indirect"></relation></listRelation></org></tutelle><tutelle active="#struct-300317" type="indirect"><org type="institution" xml:id="struct-300317" status="VALID"><orgName>Université du Havre</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle name="EA4108" active="#struct-300318" type="indirect"><org type="institution" xml:id="struct-300318" status="VALID"><orgName>Université de Rouen</orgName><desc><address><addrLine> 1 rue Thomas Becket - 76821 Mont-Saint-Aignan</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-rouen.fr/</ref></desc></org></tutelle><tutelle active="#struct-301288" type="indirect"><org type="department" xml:id="struct-301288" status="VALID"><orgName>Institut National des Sciences Appliquées - Rouen</orgName><orgName type="acronym">INSA Rouen</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-301232" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-301232" type="indirect"><org type="institution" xml:id="struct-301232" status="VALID"><orgName>Institut National des Sciences Appliquées</orgName><orgName type="acronym">INSA</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Le Havre</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université du Havre</orgName><placeName><settlement type="city">Rouen</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université de Rouen</orgName></affiliation></author></analytic><series><title level="j">Revue I3 - Information Interaction Intelligence</title><idno type="ISSN">1630-649X</idno><imprint><date type="datePub">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>analyse d'images</term><term>analyse d'images de documents</term><term>analyse de mises en page</term><term>analyse de structures de documents</term><term>champs aléatoires conditionnels</term><term>champs aléatoires de Markov</term><term>documents manuscrits</term><term>modèles discriminant</term><term>modèles génératifs</term><term>modélisation 2</term><term>modélisation markovienne</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Nous nous intéressons dans ces travaux à l'analyse d'images par le biais de modèles markoviens génératifs et discriminants : les champs de Markov et les champs aléatoires conditionnels. Pour les champs conditionnels nous présentons une implantation 2D de ces modèles principalement utilisés jusqu'à présent pour l'analyse de données monodimensionnelles. Le modèle que nous proposons repose sur une approche de type combinaison de classifieurs discriminants. Nous illustrons et comparons les capacités de cette approche par rapport aux modèles de Champs de Markov Cachés au travers d'un exemple d'application à l'analyse de la structure de documents manuscrits complexes et dégradés : les manuscrits de Flaubert.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Haute-Normandie</li><li>Rhône-Alpes</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Lyon</li><li>Rouen</li></settlement><orgName><li>Université Claude Bernard Lyon 1</li><li>Université de Lyon</li><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Nicolas, Stephane" sort="Nicolas, Stephane" uniqKey="Nicolas S" first="Stéphane" last="Nicolas">Stéphane Nicolas</name></region><name sortKey="Dardenne, Julien" sort="Dardenne, Julien" uniqKey="Dardenne J" first="Julien" last="Dardenne">Julien Dardenne</name><name sortKey="Heutte, Laurent" sort="Heutte, Laurent" uniqKey="Heutte L" first="Laurent" last="Heutte">Laurent Heutte</name><name sortKey="Paquet, Thierry" sort="Paquet, Thierry" uniqKey="Paquet T" first="Thierry" last="Paquet">Thierry Paquet</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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