A fast and general method to extract "anomalies" in an arbitrary image is proposed. The basic idea is to compute a probability density for sub-regions in an image, conditioned upon the areas surrounding the sub-regions. Linear estimation and Independent Component Analysis (ICA) are combined to obtain the probability estimates. Pseudo non-parametric correlation is used to group sets of similar surrounding patterns, from which a probability for the occurrence of a given sub-region is derived. A carefully designed multi-dimensional histogram, based on compressed vector representations, enables efficient and high-resolution extraction of anomalies from the image. Our current (unoptimized) implementation performs anomaly extraction in about 30 seconds for a 640/spl times/480 image using a 700 MHz PC. Experimental results are included that demonstrate the performance of the proposed method.


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    Title :

    Finding "anomalies" in an arbitrary image


    Contributors:
    Honda, T. (author) / Nayar, S.K. (author)


    Publication date :

    2001-01-01


    Size :

    1091589 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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