A massively parallel approach to the tracking of rigid motions in image sequences can be based on a Lie-algebra formalism. This research will extend the theoretical basis of such neural-network visual analysis, with experimental verification via a Connection Machine simulation. The key insight is that instantaneous tracking of variations in Gibson's "flow of the optic array" can be achieved by local wiring of Hoffman orbits derived from a canonical Lie decomposition. This is similar to repeated application of a Hough transform, but will here be combined with a new technique for exploiting "momentum" of the visual flow. Inspiration for this work comes from Pitts and McCulloch's theory on the extraction of universals from transformation groups. The goal is to track objects simultaneously in position and depth, without first segmenting them from the optic field.
