Computing Motion Using Resistive Networks

Christof Koch; Jin Luo; Carver Mead; James Hutchinson

doi:10.1117/12.944108

3 May 1988 Computing Motion Using Resistive Networks

Christof Koch, Jin Luo, Carver Mead, James Hutchinson

Proceedings Volume 0882, Neural Network Models for Optical Computing; (1988) https://doi.org/10.1117/12.944108
Event: 1988 Los Angeles Symposium: O-E/LASE '88, 1988, Los Angeles, CA, United States

Abstract

To us, and to other biological organisms, vision seems effortless. We open our eyes and we "see" the world in all its color; brightness, and movement. Yet, we havegreat difficulties when trying to endow our machines with similar abilities. In this paper we shall describe recent developments in the theory of early vision which lead from the formulation of the motion problem as an ill-posed one to its solution by minimizing certain "cost" functions. These cost or energy functions can be mapped onto simple analog and digital resistive networks. Thus, we shall see how the optical flow can be computed by injecting currents into resistive networks and recording the resulting stationary voltage distribution at each node. These networks can be implemented in cMOS VLSI circuits and represent plausible candidates for biological vision systems. This manuscript is a condensed version of ref.' .

Citation Download Citation

Christof Koch, Jin Luo, Carver Mead, and James Hutchinson "Computing Motion Using Resistive Networks", Proc. SPIE 0882, Neural Network Models for Optical Computing, (3 May 1988); https://doi.org/10.1117/12.944108

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