This paper presents a neural vision system for real-time obstacle detection in front of vehicles using a linear stereo vision set-up. The problem addressed here consists in identifying features in two images that are projections of the same physical entity in the three-dimensional world. The linear stereo correspondence problem is formulated as an optimization problem. An energy function, which represents the constraints on the solution, is mapped onto a two-dimensional Hopfield neural network for minimization. The system has been evaluated with experimental results on real stereo images.


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    Titel :

    Real-time neural vision for obstacle detection using linear cameras


    Beteiligte:
    Ruichek, Y. (Autor:in) / Postaire, J.-G. (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    553943 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch