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Communication Dans Un Congrès Année : 2017

ROAM: a Rich Object Appearance Model with Application to Rotoscoping

Résumé

Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition , editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline of this object. This model combines a collection of local foreground/background appearance models spread along the outline, a global appearance model of the enclosed object and a set of distinctive foreground landmarks. The structure of this rich appearance model allows simple initialization, efficient iterative optimization with exact minimization at each step, and on-line adaptation in videos. We demonstrate qualitatively and quantitatively the merit of this framework through comparisons with tools based on either dynamic seg-mentation with a closed curve or pixel-wise binary labelling.
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Dates et versions

hal-01576017 , version 1 (22-08-2017)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

  • HAL Id : hal-01576017 , version 1

Citer

Ondrej Miksik, Juan-Manuel Pérez-Rúa, Philip H S Torr, Patrick Pérez. ROAM: a Rich Object Appearance Model with Application to Rotoscoping. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul 2017, Honolulu, United States. ⟨hal-01576017⟩

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