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A Variational Approach for Object Contour Tracking

  • Nicolas Papadakis
  • Etienne Mémin
  • Frédéric Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3752)

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.

Keywords

Image Sequence Adjoint Variable Deformable Object Object Contour Gaussian Probability Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nicolas Papadakis
    • 1
  • Etienne Mémin
    • 1
  • Frédéric Cao
    • 1
  1. 1.IRISA/INRIARennesFrance

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