Show simple item record

dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.contributor.authorBenmansour, Fethallah
dc.date.accessioned2012-05-15T12:18:43Z
dc.date.available2012-05-15T12:18:43Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9216
dc.language.isoenen
dc.subjectObject extractionen
dc.subjectDigital topologyen
dc.subjectSurface meshingen
dc.subjectEnergy minimizing curvesen
dc.subjectMinimal pathsen
dc.subjectImage segmentationen
dc.subjectFast marching methoden
dc.subject.ddc006.3en
dc.titleFast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Imagesen
dc.typeCommunication / Conférence
dc.description.abstractenIn this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results.en
dc.relation.isversionofjnlnameJournal of Mathematical Imaging and Vision
dc.relation.isversionofjnlvol33
dc.relation.isversionofjnlissue2
dc.relation.isversionofjnldate2009
dc.relation.isversionofjnlpages209-221
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s10851-008-0131-0
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringer
dc.subject.ddclabelIntelligence artificielleen
dc.relation.conftitleMIA 2006en
dc.relation.confdate2006-09
dc.relation.confcityParisen
dc.relation.confcountryFranceen


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record