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dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.date.accessioned2009-07-08T08:39:34Z
dc.date.available2009-07-08T08:39:34Z
dc.date.issued2008
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/927
dc.language.isoenen
dc.subjectgeodesicsen
dc.subjectA* algorithm
dc.subjectheuristic
dc.subjectshortest paths
dc.subjectFast Marching
dc.subject.ddc519en
dc.titleHeuristically Driven Front Propagation for Fast Geodesic Extractionen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper presents a new method to quickly extract geodesic paths on images and 3D meshes. We use a heuristic to drive the front propagation procedure of the classical Fast Marching. This results in a modification of the Fast Marching algorithm that is similar to the A$^*$ algorithm used in artificial intelligence. In order to find very quickly geodesic paths between any given pair of points, two methods are proposed to devise an heuristic that restrict the front propagation. The multiresolition heuristic computes the heuristic using a propagation on a coarse map. For applications where pre-computation is acceptable, the landmark-based heuristic pre-computes distance maps to a sparse set of landmark points. We introduce various distortion metrics in order to quantify the errors introduced by the heuristically driven propagations. We show that both heuristic approaches bring a large speed-up for large scale applications that require the extraction of geodesics on images and 3D meshes.en
dc.relation.isversionofjnlnameInternational Journal for Computational Vision and Biomechanics
dc.relation.isversionofjnlvol1en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2008-01
dc.relation.isversionofjnlpages55-67en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00365292/en/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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