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dc.contributor.authorBohi, Amine
dc.contributor.authorPrandi, Dario
dc.contributor.authorGuis, Vincente
dc.contributor.authorBouchara, Frédéric
dc.contributor.authorGauthier, Jean-Paul
dc.date.accessioned2018-01-12T10:26:38Z
dc.date.available2018-01-12T10:26:38Z
dc.date.issued2016
dc.identifier.issn0924-9907
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17286
dc.language.isoenen
dc.subjectDescriptoren
dc.subjectFourier transformen
dc.subjectHexagonal griden
dc.subjectGeometric transformationsen
dc.subjectSupport vector machineen
dc.subjectObject recognitionen
dc.subject.ddc621.3en
dc.titleFourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognitionen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn this paper we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group SE(2,N)=ZN⋊R2. The proposed technique is based on generalized Fourier descriptors on the latter group, which are invariant to natural geometric transformations (rotations, translations). These descriptors are then used to feed an SVM classifier. We have tested our method against the COIL-100 image database and the ORL face database, and compared it with other techniques based on traditional descriptors, global and local. The obtained results have shown that our approach looks extremely efficient and stable to noise, in presence of which it outperforms the other techniques analyzed in the paper.en
dc.relation.isversionofjnlnameJournal of Mathematical Imaging and Vision
dc.relation.isversionofjnlvol57en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2017-01
dc.relation.isversionofjnlpages117–133en
dc.relation.isversionofdoi10.1007/s10851-016-0669-1en
dc.relation.isversionofjnlpublisherKluwer Academic Publishersen
dc.subject.ddclabelTraitement du signalen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2018-01-12T10:17:07Z
hal.person.labIds199402
hal.person.labIds60
hal.person.labIds199402
hal.person.labIds199402
hal.person.labIds199402


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