In this paper, we describe a hybrid learning system which combines a genetic algorithm with a neural network to classify grayscale images. The system operates on multi-resolution images which are formed by applying Gabor filters to a set of input images. The genetic algorithm evolves morphological probes that sample the multi-resolution images, and the perceptron algorithm then evaluates the extracted features. We demonstrate the use of this system by discriminating images of model tanks from other military vehicles. A multiplicity of accurate solutions, consisting of sparse morphological probes, are generated.<>


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    Titel :

    Multi-resolution feature extraction from Gabor filtered images


    Beteiligte:
    Rizki, M.M. (Autor:in) / Tamburino, L.A. (Autor:in) / Zmuda, M.A. (Autor:in)


    Erscheinungsdatum :

    01.01.1993


    Format / Umfang :

    697756 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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