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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    Title :

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


    Contributors:
    Kervrann (author) / Heitz (author)


    Publication date :

    1994-01-01


    Size :

    525066 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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

    Kervrann, C. / Heitz, F. / Institute of Electrical and Electronics Engineers; Computer Society | British Library Conference Proceedings | 1994


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