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hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorChen, Da
hal.structure.identifierLaboratoire de Mathématiques d'Orsay [LM-Orsay]
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorMirebeau, Jean-Marie
HAL ID: 5588
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.date.accessioned2017-11-02T11:02:52Z
dc.date.available2017-11-02T11:02:52Z
dc.date.issued2016
dc.identifier.issn0920-5691
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16892
dc.language.isoenen
dc.subjectEikonal Equation ·en
dc.subjectGeodesicen
dc.subjectPerceptual Groupingen
dc.subjectMinimal Pathen
dc.subjectCurvature Penaltyen
dc.subjectAnisotropic Fast Marching Methoden
dc.subjectEuler Elastica Curveen
dc.subjectFinsler Metricen
dc.subjectImage Segmentation ·en
dc.subjectTubular Structure Extractionen
dc.subject.ddc515en
dc.titleGlobal Minimum for a Finsler Elastica Minimal Path Approachen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn this paper, we propose a novel curvature penalized minimal path model via an orientation lifted Finsler metric and the Euler elastica curve. The original minimal path model computes the globally minimal geodesic by solving an Eikonal partial differential equation (PDE). Essentially , this first-order model is unable to penalize curvature which is related to the path rigidity property in the classical active contour models. To solve this problem, we present an Eikonal PDE-based Finsler elastica minimal path approach to address the curvature-penalized geodesic energy minimization problem. We were successful at adding the curvature penalization to the classical geodesic energy (Caselles et al, 1997; Cohen and Kimmel, 1997). The basic idea of this work is to interpret the Euler elastica bending energy via a novel Finsler elastica metric that embeds a curvature penalty. This metric is non-Riemannian, anisotropic and asym-metric, and is defined over an orientation lifted space by adding to the image domain the orientation as an extra space dimension. Based on this orientation lifting, the proposed minimal path model can benefit from both the curvature and orientation of the paths. Thanks to the fast marching method, the global minimum of the curvature-penalized geodesic energy can be computed efficiently. We introduce two anisotropic image data-driven speed functions that are computed by steerable filters. Based on these orientation-dependent speed functions, we can apply the proposed Finsler elastica minimal path model to the applications of interactive image segmentation, perceptual grouping and tubular structure extraction. Numerical experiments on both synthetic and real images show that these applications of the proposed model indeed obtain promising results.en
dc.relation.isversionofjnlnameInternational Journal of Computer Vision
dc.relation.isversionofjnlvol122en
dc.relation.isversionofjnlissue3en
dc.relation.isversionofjnldate2017-05
dc.relation.isversionofjnlpages458-483en
dc.relation.isversionofdoi10.1007/s11263-016-0975-5en
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01403941en
dc.relation.isversionofjnlpublisherKluwer Academic Publishersen
dc.subject.ddclabelAnalyseen
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.updated2017-11-02T10:56:57Z
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