Computing optical flow in resistive networks and in the primate visual system

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Abstract
It is shown how the well-known algorithm of B. Horn and B.C. Schunk (1981) for computing optical flow, based on minimizing a quadratic functional using a relaxation scheme, maps onto two different kinds of massive parallel hardware: either resistive networks which are attractive for their technological potential, or neuronal networks related to the ones occurring in the motion pathway in the primate's visual system. If the x and y components of the motion field are coded explicitly as voltages within electrical circuits, simple resistive networks solve for the optical flow in the presence of motion discontinuities. These networks are being implemented into analog, subthreshold CMOS VLSI (complementary metal oxide semiconductor very large-scale integration) circuits. If velocity is represented within a population of direction selective cells, the resulting neuronal network maps onto the primate's striate and extrastriate visual cortex (middle temporal area). The performance of the network mimicks a large number of psychological illusions as well as electrophysical findings.<>

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