Sparse Aggregation Framework for Optical Flow Estimation

Abstract : We propose a sparse aggregation framework for optical flow estimation to overcome the limitations of variational methods introduced by coarse-to-fine strategies. The idea is to compute parametric motion candidates estimated in overlapping square windows of variable size taken in the semi-local neighborhood of a given point. In the second step, a sparse representation and an optimization procedure in the continuous setting are proposed to compute a motion vector close to motion candidates for each pixel. We demonstrate the feasibility and performance of our two-step approach on image pairs and compare its performances with competitive methods on the Middlebury benchmark.
Type de document :
Communication dans un congrès
Scale Space and Variational Methods in Computer Vision, May 2015, Lège Cap Ferret, France. 〈http://link.springer.com/chapter/10.1007%2F978-3-319-18461-6_26〉
Liste complète des métadonnées

Littérature citée [27 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01138012
Contributeur : Denis Fortun <>
Soumis le : mardi 31 mars 2015 - 21:11:22
Dernière modification le : mercredi 11 avril 2018 - 01:52:32
Document(s) archivé(s) le : mardi 18 avril 2017 - 06:30:53

Fichier

sparse_aggreg.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01138012, version 1

Collections

Citation

Denis Fortun, Patrick Bouthemy, Charles Kervrann. Sparse Aggregation Framework for Optical Flow Estimation. Scale Space and Variational Methods in Computer Vision, May 2015, Lège Cap Ferret, France. 〈http://link.springer.com/chapter/10.1007%2F978-3-319-18461-6_26〉. 〈hal-01138012〉

Partager

Métriques

Consultations de la notice

149

Téléchargements de fichiers

163