A. Jepson and M. Black, Mixture models for optical flow, Tech. Report, Res. in Biol. and Comp. Vision, Dept. of Comp. Sci., Univ. of Toronto, RBCV-TR-93-44, 1993.
Postscript. See also:
A. Jepson and M. Black, Mixture models for optical flow computation, in Proceedings of the DIMACS Worshop on Partitioning Data Sets: With Applications to Psychology, Vision and Target Tracking, Eds Ingmar Cox, Pierre Hansen, and Bela Julesz, AMS Pub., Providence, RI.
And:
A. Jepson and M. Black, Mixture models for optical flow, Proc of IEEE CVPR, New York, 1993, pp.760-761.

Abstract: The computation of optical flow relies on merging information available over an image patch to form an estimate of 2D image velocity at a point. This merging process raises a host of issues, which include the treatment of outliers in component velocity measurements and the modeling of multiple motions within a patch which arise from occlusion boundaries or transparency. We present a new approach which allows us to deal with these issues within a common framework. Our approach is based on the use of a probabilistic mixture model to explicitly represent multiple motions within a patch. We use a simple extension of the EM-algorithm to compute a maximum likelihood estimate for the various motion parameters. Preliminary experiments indicate that this approach is computationally efficient and can provide robust estimates of the optical flow values in the presence of outliers and multiple motions. The basic approach can also be applied to other problems in computational vision, such as the computation of 3D relative motion, which require the integration of several partial constraints to obtain a desired quantity.