  The pel-recursive computation of 2-D optical flow has been extensively
studied in computer vision to estimate motion from image sequences, but it
still raises a wealth of issues, such as the treatment of outliers, motion
discontinuities and occlusion. It relies on spatio-temporal brightness
variations due to motion. Our proposed adaptive regularized approach deals with
these issues within a common framework. It relies on the use of a data-driven
technique called Mixed Norm (MN) to estimate the best motion vector for a given
pixel. In our model, various types of noise can be handled, representing
different sources of error. The motion vector estimation takes into
consideration local image properties and it results from the minimization of a
mixed norm functional with a regularization parameter depending on the
kurtosis. This parameter determines the relative importance of the fourth norm
and makes the functional convex. The main advantage of the developed procedure
is that no knowledge of the noise distribution is necessary. Experiments
indicate that this approach provides robust estimates of the optical flow.
