Previously (ARVO-92), we outlined a model of transparent motion perception that used layers to represent multiple motions. Locally, the model tested for the presence of a particular velocity despite the presence of other velocities at the same location. This was accomplished by applying first-order "nulling" filters to remove the energy due to a possible conflicting velocity, and then testing for the chosen velocity.

Here we present a new method for testing the presence of a local velocity in an image, using "donut" mechanisms formed from the weighted combination of spatio-temporal energy units. This method has the advantage over nulling filters that it does not require the application of multiple prefilters for each tested velocity, can potentially handle regions with 3 motions, and seems to be more biologically plausible.

Our global layer selection mechanism attempts to account for the local velocity distributions with a small set of global basis functions (translations, expansions, rotations). Using donut mechanisms permits a simplified layer selection optimization, in which inhibition between basis functions is determined by the product of their weight coefficients. With this scheme, we demonstrate the decomposition of image sequences containing additively combined multiple moving objects into a set of layers corresponding to each object.