A Maximum-Flow Model for Digital Elastica Shape Optimization - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

A Maximum-Flow Model for Digital Elastica Shape Optimization

Résumé

The Elastica is a curve regularization model that integrates the squared curvature in addition to the curve length. It has been shown to be useful for contour smoothing and interpolation, for example in the presence of thin elements. In this article, we propose a graph-cut based model for optimizing the discrete Elastica energy using a fast and efficient graph-cut model. Even though the Elastica energy is neither convex nor sub-modular, we show that the final shape we achieve is often very close to the globally optimal one. Our model easily adapts to image segmentation tasks. We show that compared to previous works and state-of-the-art algorithms, our proposal is simpler to implement, faster, and yields comparable or better results.
Fichier principal
Vignette du fichier
discrete_elastica_dgmm_2021.pdf (6.18 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04580405 , version 1 (20-05-2024)

Identifiants

Citer

Daniel Martins Antunes, Jacques-Olivier Lachaud, Hugues Talbot. A Maximum-Flow Model for Digital Elastica Shape Optimization. Discrete Geometry Mathematical Morphology 2021, May 2021, Uppsala (Suède), Sweden. pp.429-440, ⟨10.1007/978-3-030-76657-3_31⟩. ⟨hal-04580405⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More