Building on recent progress in modeling filter response statistics of natural images we integrate a statistical model into a variational framework for image segmentation. Incorporated in a sound probabilistic distance measure the model drives level sets toward meaningful segmentations of complex textures and natural scenes. Since each region comprises two model parameters only the approach is computationally efficient and enables the application of variational segmentation to a considerably larger class of real-world images. We validate the statistical basis of our approach on thousands of natural images and demonstrate that our model outperforms recent variational segmentation methods based on second-order statistics.


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    Title :

    Natural image statistics for natural image segmentation


    Contributors:
    Heiler, (author) / Schnorr, (author)


    Publication date :

    2003-01-01


    Size :

    414558 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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