An early-processing algorithm to extract motion information from closely sampled image sequences is presented. This method outputs dense displacement field, based on the extraction of strips in slices taken from the image volume along the temporal dimension. The extracted strips provide estimates of the velocity component along the slice orientation. Because of the high sampling rate, the motion is assumed to be piecewisely translational. A voting scheme to estimate the position of FOE while extracting strips is proposed. The true velocity can then be calculated. Results on several real image sequences and a promising speedup from the parallel implementation on the Connection Machine are presented.>

A method is presented for analyzing a sequence of closely sampled images, forming a spatio-temporal volume. It is proposed to find the normal velocity component of each edge point by making assumptions about this scene, such as smooth motion, common motion and constant illumination. The resulting normal velocity field can then be used to resolve the real velocity field and to perform segmentation. The system is quite robust and is capable of handling occlusion as well as disocclusion. Results are presented on synthetic data consisting of two objects moving with occlusion and on real image sequences.>