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New Elastica Geodesic Approach with Convexity Shape Prior for Region-based Active Contours and Image Segmentation

Chen, Da; Cohen, Laurent D.; Mirebeau, Jean-Marie; Tai, Xue-Cheng (2021), New Elastica Geodesic Approach with Convexity Shape Prior for Region-based Active Contours and Image Segmentation, ICCV 21, International Conference on Computer VIsion, 2021-10, Montreal, CANADA

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8098.pdf (1.264Mb)
Type
Communication / Conférence
Lien vers un document non conservé dans cette base
https://hal.archives-ouvertes.fr/hal-03174123
Date
2021
Titre du colloque
ICCV 21, International Conference on Computer VIsion
Date du colloque
2021-10
Ville du colloque
Montreal
Pays du colloque
CANADA
Métadonnées
Afficher la notice complète
Auteur(s)
Chen, Da
Cohen, Laurent D.
Mirebeau, Jean-Marie
Tai, Xue-Cheng
Résumé (EN)
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Currently, existing geodesic-based segmentation approaches usually exploit the image features in conjunction with regularization terms, such as curve length, for computing geodesic paths. In this paper, we consider a more complicated problem: finding simple closed geodesic curves which are imposed a convexity shape prior. The proposed approach relies on an orientation-lifting strategy, by which a planar curve can be mapped to an high-dimensional orientation space. The convexity shape priors serve as a constraint for the construction of local metrics in the lifted space. The geodesic curves then can be efficiently computed through the single-pass Fast Marching method (FMM). In addition, we introduce a way to incorporate region-based homogeneity features into the proposed geodesic model so as to solve the region-based segmentation issues with shape prior constraints.
Mots-clés
Active contours; Convexity shape prior; Curvature penalization; Eikonal equation

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Affichage des éléments liés par titre et auteur.

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