A Hierarchical Statistical Framework for the Segmentation ofDeformable Objects in Image SequencesCharles

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 template. Global deformations are modeled using a Karhunen Loeve expansion of the distorsions observed on a representative population. Local deformations are modeled by a ((rst-order) Markov process. The optimal bayesian estimate of the global and local deformations is obtained by maximizing a non-linear joint probability distribution using stochastic and deterministic optimization techniques. The use of global optimization techniques yields robust and reliable segmentations in adverse situations such as low signal-to-noise ratio, non-gaussian noise or occlusions. Moreover , no human interaction is required to initialize the model. The approach is demonstrated on synthetic as well as on real-world image sequences showing moving hands with partial occlusions.