A CNN solution for depth estimation from binocular stereo imagery

Novel results and experiments are presented on the application of cellular neural networks to binocular stereo vision. A CNN universal machine algorithm is described for depth estimation as a part of a stereo-vision-based guidance system for autonomous vehicles. Being most amenable to revealing stereo correspondence, extraction of vertical edges is performed first. Then their distance from the observer in 3-D space is established through a stereo matching scheme. The performance of the algorithm is demonstrated on real-life highway imagery and it is shown that very low latency real-time operation is attainable via the CNN-UM.