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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    Title :

    Real-time neural vision for obstacle detection using linear cameras


    Contributors:


    Publication date :

    1995-01-01


    Size :

    553943 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English