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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    A neural network architecture for texture segmentation and labelling


    Weitere Titelangaben:

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


    Beteiligte:
    Dupaguntla, N.R. (Autor:in) / Vemuri, V. (Autor:in)


    Erscheinungsdatum :

    1989


    Format / Umfang :

    7 Seiten, 14 Quellen


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Texture and Neural Network for Road Segmentation

    Fernamdez-Maloigne, C. / Bonnet, W. / IEEE et al. | British Library Conference Proceedings | 1995


    Road Texture Segmentation with a Neural Network

    Bonnet, W. / Fernandez-Maloigne, C. / Koudella, C. | British Library Conference Proceedings | 1997


    Unsupervised texture image segmentation by improved neural network ART2

    Wang, Zhiling / Labini, G. / Mugnuolo, R. et al. | AIAA | 1994


    1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

    Koushik Chennakesavan / Magnus A Haw / Alexandre Quintart | NTRS


    1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

    Koushik Chennakesavan / Magnus A. Haw / Alexandre Quintart | NTRS