  The computation of 2-D optical flow by means of regularized pel-recursive
algorithms raises a host of issues, which include the treatment of outliers,
motion discontinuities and occlusion among other problems. We propose a new
approach which allows us to deal with these issues within a common framework.
Our approach is based on the use of a technique called Generalized
Cross-Validation to estimate the best regularization scheme for a given pixel.
In our model, the regularization parameter is a matrix whose entries can
account for diverse sources of error. The estimation of the motion vectors
takes into consideration local properties of the image following a spatially
adaptive approach where each moving pixel is supposed to have its own
regularization matrix. Preliminary experiments indicate that this approach
provides robust estimates of the optical flow.
