A hierarchical statistical framework for the segmentation of deformable objects in image sequences

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Abstract
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 distortions observed on a representative population. Local deformations are modeled by a (first-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.

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