A variational method for curve extraction with curvature-dependent energies
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911296312573952 |
|---|---|
| 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 |