Empirical choice of smoothing parameters in optical flow with correlated errors

Optical flow estimation algorithms such as the Lukas-Kanade method and Horn and Schunk method require selection of a tuning parameter. In the former case a neighbourhood size, in the latter, a penalty parameter. Selection of these tuning parameters is difficult in general but has a profound effect on the results. So automatic methods of selection are of great interest. In previous work we have developed such methods based on white noise assumptions and here we show how to adjust for the effect of spatially correlated errors. These always occur in practice and can degrade the performance of white noise based procedures.