Treffer: A variational method for curve extraction
Title:
A variational method for curve extraction
Authors:
Contributors:
École nationale des ponts et chaussées (ENPC), Méthodes numériques pour le problème de Monge-Kantorovich et Applications en sciences sociales (MOKAPLAN), CEntre de REcherches en MAthématiques de la DEcision (CEREMADE), Université Paris Dauphine-PSL, Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Dauphine-PSL, Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), ANR-23-PEIA-0004,PDE-AI,Numerical analysis, optimal control and optimal transport for AI(2023)
Source:
SSVM 2025 (à paraitre) ; SSVM 2025 - 10th Scale-Space and Variational Methods in Computer Vision ; https://hal.science/hal-04980911 ; SSVM 2025 - 10th Scale-Space and Variational Methods in Computer Vision, May 2025, Totnes, Devon, United Kingdom
Publisher Information:
CCSD
Publication Year:
2025
Subject Terms:
Subject Geographic:
Document Type:
Konferenz
conference object
Language:
English
Availability:
Rights:
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.FCF81A81
Database:
BASE
Weitere Informationen
International audience ; We propose a functional for extracting curves between a list of possible endpoints, based on a discretization of a variational energy and Smirnov's decomposition theorem for vector fields. It is then used to design a bi-level minimization approach to automatically extract curves and 1D structures from an image, which is mostly unsupervised.