Dynamic models for wavelet representations of shape

Abstract

In this paper, a dynamic model for contours using wavelets is presented. First it is shown how to construct probabilistic shape priors for modeling contour deformation using wavelets. Then a dynamic model for shape evolution in time is presented. This allows this formulation to be applied to the problem of tracking a contour using the stochastic model to predict contour location and appearance in successive image frames. Computational results for two real image problems are given for the Condensation (Conditional Density Propagation) tracking algorithm. It is shown that this formulation successfully tracks the objects in the image sequences.