An essential feature of a practical automatic image recognition system is the ability to tolerate certain types of variations within images. The recognition of images subject to intrinsic variations can be treated as a sorting task in which an image is identified as a member of some class of images. Herein, the maximum-likelihood strategy, an important tool in the field of statistical decision theory, is applied to the image classification problem. We show that the strategy can be implemented in a standard image correlation system and that excellent classification results can be obtained.


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    Titel :

    Maximum-Likelihood Image Classification


    Beteiligte:
    Wernick, Miles N. (Autor:in) / Morris, G. M. (Autor:in)

    Kongress:

    Digital and Optical Shape Representation and Pattern Recognition ; 1988 ; Orlando,FL,United States


    Erschienen in:

    Erscheinungsdatum :

    1988-08-22





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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