A Hierarchical Statistical Framework for the Segmentation of Deformable Objects in Image Sequences

: In this paper, we propose a new statistical framework for modeling and extracting 2D moving deformable objects from image sequences. The object representation relies on a hierarchical description of the deformations applied to a computed template. Global deformations are modeled using a Karhunen Loeve expansion of the distorsions observed on a representative population. Local deformations are modeled using (first-order) Markov processes. The statistical hierarchical model is used to represent the a priori structure of the shapes to be extracted from the image sequence. The optimal bayesian estimate of the global and local deformations is obtained by minimizing a global objective function depending on the global deformation parameters and on the local random deformation process. A partial optimal solution is estimated by stochastic and deterministic relaxation procedures. The use of global optimization algorithms yields robust and reliable segmentations in adverse situations such as l..