Finite-differences discretizations of the Mumford-Shah functional

نویسندگان

  • ANTONIN CHAMBOLLE
  • A. CHAMBOLLE
چکیده

About two years ago, Gobbmo [21] gave a proof of a De Giorgi's conjecture on the approximation of the Mumford-Shah energy by means of finite-differences based non-local functionals In this work, we introducé a discretized version of De Giorgi's approximation, that may be seen as a generahzation of Blake and Zisserman's "weak membrane" energy (first mtroduced m the image segmentation framework) A simple adaptation of Gobbmo's resuit s allows us to compute the F-limit of this discrete functional as the discretîzation step goes to zero, this generahzes a previous work by the author on the "weak membrane" model [10] We deduce how to design m a systematic way discrete image segmentation functionals with "less anisotropy" than Blake and Zisserman's original energy, and we show m some numerical experiments how ît improves the method Résumé. Une conjecture récente de De Giorgi, sur l'approximation de la fonctionnelle de Mumford et Shah par des fonctionnelles non locales basées sur des différences finies, a été démontrée il y a un peu plus de deux ans par Gobbmo [21] Nous introduisons dans ce travail une version discrétisée de l'approximation de De Giorgi, que l'on peut voir comme une généralisation de l'énergie de "membrane faible" introduite par Blake et Zisserman pour la segmentation d'images Une adaptation élémentaire des démonstrations de Gobbmo permet de calculer la F-hmite de cette approximation discrète, lorsque le pas de discrétisation tend vers zéro , ce calcul généralise un résultat précédent de l'auteur sur l'énergie de "membrane faible" [10] On déduit ainsi une manière de construire systématiquement des fonctionnelles de segmentation d'images "moins anisotropes" que l'énergie originale de Blake et Zisserman, et l'amélioration obtenue est illustrée par des expériences numériques AMS Subject Classification. 26A45, 49J45, 49Q20, 68U10 Received September 22, 1997 Revised February 15, 1998

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تاریخ انتشار 2017