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C. Kervrann. Learning probabilistic deformation models from image sequences. Signal Processing, (71):155-171, 1998.

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In This paper, we present an approach for an unsupervised learning of probabilistic deformation modes of 2D moving objects from images sequences. The object representation relies on a statistical description of global and local deformations applied to an a priori prototype shape. The optimal Bayesian estimate of the deformation process is obtained by maximizing a nonlinear joint probability distribution using stochastic and deterministic optimization techniques. The estimates obtained at time t are integrated in the deformation model as a priori knowledge for the segmentation at time \textit{t} + 1. Deformation modes are updated on-line using a principal component analysis of the distortions computed from the shapes estimated previously in the image sequence. In addition, the updated deformation modes are exploited for the real-world image sequences showing the tracking of hands undergoing 2D articulated movements


Charles Kervrann

BibTex Reference

   Author = {Kervrann, C.},
   Title = {Learning probabilistic deformation models from image sequences},
   Journal = {Signal Processing},
   Number = {71},
   Pages = {155--171},
   Year = {1998}

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