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Hauptverfasser: Arthaud, Majid, Chambolle, Antonin, Duval, Vincent
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2512.01494
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author Arthaud, Majid
Chambolle, Antonin
Duval, Vincent
author_facet Arthaud, Majid
Chambolle, Antonin
Duval, Vincent
contents We introduce a variational approach for extracting curves between a list of possible endpoints, based on the discretization of an energy and Smirnov's decomposition theorem for vector fields. It is used to design a bi-level minimization approach to automatically extract curves and 1D structures from an image, which is mostly unsupervised. We extend then the method to curvature-dependent energies, using a now classical lifting of the curves in the space of positions and orientations equipped with an appropriate sub-Riemanian or Finslerian metric.
format Preprint
id arxiv_https___arxiv_org_abs_2512_01494
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A variational method for curve extraction with curvature-dependent energies
Arthaud, Majid
Chambolle, Antonin
Duval, Vincent
Computer Vision and Pattern Recognition
We introduce a variational approach for extracting curves between a list of possible endpoints, based on the discretization of an energy and Smirnov's decomposition theorem for vector fields. It is used to design a bi-level minimization approach to automatically extract curves and 1D structures from an image, which is mostly unsupervised. We extend then the method to curvature-dependent energies, using a now classical lifting of the curves in the space of positions and orientations equipped with an appropriate sub-Riemanian or Finslerian metric.
title A variational method for curve extraction with curvature-dependent energies
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2512.01494