A variational approach for object contour tracking

Nicolas Papadakis 1 Etienne Mémin 1 Frederic Cao 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this paper we describe a new framework for the tracking of closed curves described through implicit surface modeling. The approach proposed here enables a continuous tracking along an image sequence of deformable object contours. Such an approach is formalized through the minimization of a global spatio-temporal continuous cost functional stemming from a Bayesian Maximum a posteriori estimation of a Gaussian probability distribution. The resulting minimization sequence consists in a forward integration of an evolution law followed by a backward integration of an adjoint evolution model. This latter pde include also a term related to the discrepancy between the curve evolution law and a noisy observation of the curve. The efficiency of the approach is demonstrated on image sequences showing deformable objects of different natures.
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Nicolas Papadakis, Etienne Mémin, Frederic Cao. A variational approach for object contour tracking. ICCV Workshop on Variational, Geometric and Level Set Methods in Computer Vision (VLSM'05), Oct 2005, Beijing, China. pp.259-270. ⟨hal-00655852⟩

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