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dc.contributor.authorChen, Da
dc.contributor.authorMirebeau, Jean-Marie
dc.contributor.authorCohen, Laurent D.
dc.date.accessioned2020-06-11T09:18:55Z
dc.date.available2020-06-11T09:18:55Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20874
dc.language.isoenen
dc.subjectMeasurementen
dc.subjectComputational modelingen
dc.subjectMathematical modelen
dc.subjectActive contoursen
dc.subjectImage segmentationen
dc.subjectForceen
dc.subjectFeature extractionen
dc.subject.ddc515en
dc.titleA New Finsler Minimal Path Model with Curvature Penalization for Image Segmentation and Closed Contour Detectionen
dc.typeCommunication / Conférence
dc.description.abstractenIn this paper, we propose a new curvature penalized minimal path model for image segmentation via closed contour detection based on the weighted Euler elastica curves, firstly introduced to the field of computer vision in [22]. Our image segmentation method extracts a collection of curvature penalized minimal geodesics, concatenated to form a closed contour, by connecting a set of user-specified points. Globally optimal minimal paths can be computed by solving an Eikonal equation. This first order PDE is traditionally regarded as unable to penalize curvature, which is related to the path acceleration in active contour models. We introduce here a new approach that enables finding a global minimum of the geodesic energy including a curvature term. We achieve this through the use of a novel Finsler metric adding to the image domain the orientation as an extra space dimension. This metric is non-Riemannian and asymmetric, defined on an orientation lifted space, incorporating the curvature penalty in the geodesic energy. Experiments show that the proposed Finsler minimal path model indeed outperforms state-of-the-art minimal path models in both synthetic and real images.en
dc.relation.ispartoftitle2016 IEEE Conference on Computer Vision and Pattern Recognitionen
dc.relation.ispartofpublnameIEEEen
dc.subject.ddclabelAnalyseen
dc.relation.conftitle2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)en
dc.relation.confdate2016-06
dc.relation.confcityLas Vegasen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/CVPR.2016.45en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-06-11T09:14:23Z
hal.person.labIds60
hal.person.labIds60
hal.person.labIds60


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