The authors describe a simple, parallel algorithm that successfully computes an optical flow from sequences of real images, is consistent with human psychophysics, and suggests a plausible physiological model. Regularizing optical flow computation leads to a formulation which minimizes matching error and, at the same time, maximizes smoothness of the optical flow. The authors develop an approximation to the full regularization computation in which corresponding points are found by comparing local patches of images. Selection among competing matches is performed using a winner-take-all scheme. The algorithm is independent of the types of features used for matching. Experiments with natural images show that the scheme is effective and robust against noise. The algorithm shows several of the same 'illusions' that humans perceive. A natural physiological implementation of the model is consistent with data from cortical areas V1 and MT.