Using 3D Spline Differentiation to Compute Quantitative Optical Flow

Abstract : 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.
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Communication dans un congrès
Third Canadian Conference on Computer and Robot Vision, CRV 2006, Jun 2006, Québec, Canada. IEEE, 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06) 〈http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10921〉. 〈10.1109/CRV.2006.84〉
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https://hal.archives-ouvertes.fr/hal-01311458
Contributeur : Marc Daniel <>
Soumis le : mercredi 4 mai 2016 - 11:36:15
Dernière modification le : jeudi 15 mars 2018 - 16:56:06

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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, Jun 2006, Québec, Canada. IEEE, 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06) 〈http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10921〉. 〈10.1109/CRV.2006.84〉. 〈hal-01311458〉

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