A new neural real-time implementation for obstacle detection using linear stereo vision

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