A Unified Framework for Detecting Groups and Application to Shape Recognition
Identifieur interne : 004E58 ( Main/Merge ); précédent : 004E57; suivant : 004E59A Unified Framework for Detecting Groups and Application to Shape Recognition
Auteurs : Frédéric Cao [France] ; Julie Delon [France] ; Agnès Desolneux [France] ; Pablo Musé [France] ; Frédéric Sur [France]Source :
- Journal of Mathematical Imaging and Vision [ 0924-9907 ] ; 2007-02-01.
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Abstract
Abstract: A unified a contrario detection method is proposed to solve three classical problems in clustering analysis. The first one is to evaluate the validity of a cluster candidate. The second problem is that meaningful clusters can contain or be contained in other meaningful clusters. A rule is needed to define locally optimal clusters by inclusion. The third problem is the definition of a correct merging rule between meaningful clusters, permitting to decide whether they should stay separate or unite. The motivation of this theory is shape recognition. Matching algorithms usually compute correspondences between more or less local features (called shape elements) between images to be compared. Each pair of matching shape elements leads to a unique transformation (similarity or affine map.) The present theory is used to group these shape elements into shapes by detecting clusters in the transformation space.
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DOI: 10.1007/s10851-006-9176-0
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1007/s10851-006-9176-0</idno><series><title level="j">Journal of Mathematical Imaging and Vision</title><idno type="ISSN">0924-9907</idno><imprint><date type="datePub">2007</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>a contrario detection</term><term>clustering</term><term>perceptual grouping</term><term>shape recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A unified a contrario detection method is proposed to solve three classical problems in clustering analysis. The first one is to evaluate the validity of a cluster candidate. The second problem is that meaningful clusters can contain or be contained in other meaningful clusters. A rule is needed to define locally optimal clusters by inclusion. The third problem is the definition of a correct merging rule between meaningful clusters, permitting to decide whether they should stay separate or unite. The motivation of this theory is shape recognition. Matching algorithms usually compute correspondences between more or less local features (called shape elements) between images to be compared. Each pair of matching shape elements leads to a unique transformation (similarity or affine map.) The present theory is used to group these shape elements into shapes by detecting clusters in the transformation space.</div></front></TEI></HAL><ISTEX><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Unified Framework for Detecting Groups and Application to Shape Recognition</title><author><name sortKey="Cao, Frederic" sort="Cao, Frederic" uniqKey="Cao F" first="Frédéric" last="Cao">Frédéric Cao</name></author><author><name sortKey="Delon, Julie" sort="Delon, Julie" uniqKey="Delon J" first="Julie" last="Delon">Julie Delon</name></author><author><name sortKey="Desolneux, Agnes" sort="Desolneux, Agnes" uniqKey="Desolneux A" first="Agnès" last="Desolneux">Agnès Desolneux</name></author><author><name sortKey="Muse, Pablo" sort="Muse, Pablo" uniqKey="Muse P" first="Pablo" last="Musé">Pablo Musé</name></author><author><name sortKey="Sur, Frederic" sort="Sur, Frederic" uniqKey="Sur F" first="Frédéric" last="Sur">Frédéric Sur</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:B0CD41756D492587AB0466612EACA9E49CEC6B26</idno><date when="2007" year="2007">2007</date><idno type="doi">10.1007/s10851-006-9176-0</idno><idno type="url">https://api.istex.fr/ark:/67375/VQC-46VZS1Z0-P/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">002991</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">002991</idno><idno type="wicri:Area/Istex/Curation">002954</idno><idno type="wicri:Area/Istex/Checkpoint">001109</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">001109</idno><idno type="wicri:doubleKey">0924-9907:2007:Cao F:a:unified:framework</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">A Unified Framework for Detecting Groups and Application to Shape Recognition</title><author><name sortKey="Cao, Frederic" sort="Cao, Frederic" uniqKey="Cao F" first="Frédéric" last="Cao">Frédéric Cao</name><affiliation wicri:level="1"><country xml:lang="fr">France</country><wicri:regionArea>IRISA/INRIA</wicri:regionArea></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author><author><name sortKey="Delon, Julie" sort="Delon, Julie" uniqKey="Delon J" first="Julie" last="Delon">Julie Delon</name><affiliation wicri:level="3"><country xml:lang="fr">France</country><wicri:regionArea>LTCI, Télécom Paris (CNRS UMR 5141), 46, rue Barrault, F-75634, Paris cedex 13</wicri:regionArea><placeName><region type="region" nuts="2">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author><author><name sortKey="Desolneux, Agnes" sort="Desolneux, Agnes" uniqKey="Desolneux A" first="Agnès" last="Desolneux">Agnès Desolneux</name><affiliation wicri:level="1"><country xml:lang="fr">France</country><wicri:regionArea>MAP5/CNRS</wicri:regionArea></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author><author><name sortKey="Muse, Pablo" sort="Muse, Pablo" uniqKey="Muse P" first="Pablo" last="Musé">Pablo Musé</name><affiliation wicri:level="1"><country xml:lang="fr">France</country><wicri:regionArea>CMLA, ENS-Cachan</wicri:regionArea><wicri:noRegion>ENS-Cachan</wicri:noRegion><wicri:noRegion>ENS-Cachan</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author><author><name sortKey="Sur, Frederic" sort="Sur, Frederic" uniqKey="Sur F" first="Frédéric" last="Sur">Frédéric Sur</name><affiliation wicri:level="3"><country xml:lang="fr">France</country><wicri:regionArea>Loria, Bâtiment C - projet Magrit, Campus Scientifique - BP 239, 54506, Vandoeuvre-lès-Nancy cedex</wicri:regionArea><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Mathematical Imaging and Vision</title><title level="j" type="abbrev">J Math Imaging Vis</title><idno type="ISSN">0924-9907</idno><idno type="eISSN">1573-7683</idno><imprint><publisher>Kluwer Academic Publishers</publisher><pubPlace>Boston</pubPlace><date type="published" when="2007-02-01">2007-02-01</date><biblScope unit="volume">27</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="91">91</biblScope><biblScope unit="page" to="119">119</biblScope></imprint><idno type="ISSN">0924-9907</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0924-9907</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>a contrario detection</term><term>clustering</term><term>perceptual grouping</term><term>shape recognition</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: A unified a contrario detection method is proposed to solve three classical problems in clustering analysis. The first one is to evaluate the validity of a cluster candidate. The second problem is that meaningful clusters can contain or be contained in other meaningful clusters. A rule is needed to define locally optimal clusters by inclusion. The third problem is the definition of a correct merging rule between meaningful clusters, permitting to decide whether they should stay separate or unite. The motivation of this theory is shape recognition. Matching algorithms usually compute correspondences between more or less local features (called shape elements) between images to be compared. Each pair of matching shape elements leads to a unique transformation (similarity or affine map.) The present theory is used to group these shape elements into shapes by detecting clusters in the transformation space.</div></front></TEI></ISTEX></double></record>Pour manipuler ce document sous Unix (Dilib)
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