Using 3D Spline Differentiation to Compute Quantitative Optical Flow - Archive ouverte HAL
Communication Dans Un Congrès Année : 2006

Using 3D Spline Differentiation to Compute Quantitative Optical Flow

John Barron
  • Fonction : Auteur
University of Western Ontario
Marc Daniel
GMOD-LSIS
CV
Jean-Luc Mari
GMOD-LSIS
CV

Résumé

We show that differentiation via fitting B-splines to the spatio-temporal intensity data comprising an image sequence provides at least the same and usually better 2D Lucas and Kanade optical flow than that computed via Simoncelli’s balanced/matched filters.

Domaines

Autre [cs.OH]

Marc Daniel  : Connectez-vous pour contacter le contributeur

https://hal.science/hal-01311458

Soumis le : mercredi 4 mai 2016-11:36:15

Dernière modification le : mardi 5 décembre 2023-18:08:08

Fichier non déposé

Dates et versions

hal-01311458 , version 1 (04-05-2016)

Identifiants

Citer

John Barron, Marc Daniel, Jean-Luc Mari. Using 3D Spline Differentiation to Compute Quantitative Optical Flow. Third Canadian Conference on Computer and Robot Vision, CRV 2006, CIPPRS, Jun 2006, Québec, Canada. ⟨10.1109/CRV.2006.84⟩. ⟨hal-01311458⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

UNIV-TLN CNRS UNIV-AMU LSIS-GMOD LIS-LAB IRENAV LAMPA LCPI LABOMAP LISPEN MSMP
55 Consultations
0 Téléchargements

Altmetric

Partager

More