Robust optical flow detection based on the distance transform with the CNN nonlinear circuits

A robust optical flow computation algorithm utilizing the trajectories of feature points has been developed. For some applications of optical flows, correct optical flows (though they are not so many) are more useful than unreliable ones at every pixel point. The proposed algorithm is for detecting the optical flows only at the feature points. The optical flow vectors are extracted from the trajectory segments of feature points on which distance information is developed through a distance transform. A multi-layer cellular neural network (CNN) structure and nonlinear templates for the proposed algorithm are suggested and examined. Simulation results show that the proposed algorithm is robust against noise, even without any preprocessing.