A correlation-relaxation-labeling framework for computing optical flow-template matching from a new perspective

Optical flow estimation is discussed based on a model for time-varying images more general than that implied by Horn and Schunk (1981). The emphasis is on applications where low contrast imagery, nonrigid or evolving object patterns movement, as well as large interframe displacements are encountered. Template matching is identified as having advantages over point correspondence and the gradient-based approach in dealing with such applications. The two fundamental uncertainties in feature matching, whether template matching or feature point correspondences, are discussed. Correlation template matching procedures are established based on likelihood measurement. A method for determining optical flow is developed by combining template matching and relaxation labeling. A number of candidate displacements for each template and their respective likelihood measures are determined. Then, relaxation labeling is employed to iteratively update each candidate´s likelihood by requiring smoothness within a motion field. Real cloud images from satellites are used to test the method