A neural network architecture and training procedure that are suitable for texture segmentation and labeling are developed. The underlying premise of the approach is that texture segmentation can be achieved by recognizing local differences in texels. The proposed architecture comprises a feature extraction network and a texture discrimination network, which is really a variation of T. Kohonen's adaptive learning network (Self-Organization and Associative Memory, Springer-Verlag, 1984). Results from a preliminary computer simulation are presented.


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    Title :

    A neural network architecture for texture segmentation and labelling


    Additional title:

    Analoge Möglichkeiten des BSB-Modells am Beispiel die nicht-strahlenden Umkehr-Flugkörper Problems


    Contributors:


    Publication date :

    1989


    Size :

    7 Seiten, 14 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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