In binocular stereo, the objective is to establish the matching of a pair of images. In the motion case, the objective is to compute the displacement field from consecutive images of a moving scene. Optical flow is an approximation of small inter-frame displacement. Two methods have been developed. The first method is based on multiple image attributes and is among the first algorithms which can deal with large image disparities and compute dense 3-D depth maps. The second method is based on the windowed Fourier phase (WFP). The WFP is quasi-linear and spatially dense, with spatial period and slope controlled by the selected analysis frequency. The WFP includes the zero-crossings and the peaks as special cases, but it contains much more information essential for stable matching. Theoretically, the WFP is complete in representing the signals up to a multiplicative constant. The implementation of the matching algorithm resembles that of a neural network and is well suited for commercially available parallel frame-rate hardware. Experiments have achieved good results with random-dot images and natural images, used either as stereograms or as consecutive views of a moving scene.