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Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierEquipe Image - Laboratoire GREYC - UMR6072
dc.contributor.authorRABIN, Julien
dc.date.accessioned2024-04-04T03:00:17Z
dc.date.available2024-04-04T03:00:17Z
dc.date.issued2017
dc.identifier.issn0924-9907
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192805
dc.description.abstractEnWe investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory. In this setting, several transport cost functions are considered and used to match statistical distributions of features. In practice, global multidimensional histograms are estimated from the segmented image regions and are compared to reference models that are either fixed histograms given a priori, or directly inferred in the non-supervised case. The different convex problems studied are solved efficiently using primal--dual algorithms. The proposed approach is generic and enables multiphase segmentation as well as co-segmentation of multiple images.
dc.description.sponsorshipGeneralized Optimal Transport Models for Image processing - ANR-16-CE33-0010
dc.language.isoen
dc.publisherSpringer Verlag
dc.title.enConvex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost
dc.typeArticle de revue
dc.identifier.doi10.1007/s10851-017-0725-5
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
bordeaux.journalJournal of Mathematical Imaging and Vision
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01533657
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01533657v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Mathematical%20Imaging%20and%20Vision&rft.date=2017&rft.eissn=0924-9907&rft.issn=0924-9907&rft.au=PAPADAKIS,%20Nicolas&RABIN,%20Julien&rft.genre=article


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