Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.


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    Title :

    Optimal sub-shape models by minimum description length


    Contributors:
    Langs, G. (author) / Peloschek, P. (author) / Bischof, H. (author)


    Publication date :

    2005-01-01


    Size :

    660076 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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