Real-time neural vision for obstacle detection using linear cameras

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.