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Proceedings Paper

Computing Motion Using Resistive Networks
Author(s): Christof Koch; Jin Luo; Carver Mead; James Hutchinson
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Paper 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.' .

Paper Details

Date Published: 3 May 1988
PDF: 6 pages
Proc. SPIE 0882, Neural Network Models for Optical Computing, (3 May 1988); doi: 10.1117/12.944108
Show Author Affiliations
Christof Koch, California Institute of Technology (United States)
Jin Luo, California Institute of Technology (United States)
Carver Mead, California Institute of Technology (United States)
James Hutchinson, California Institute of technology (United States)


Published in SPIE Proceedings Vol. 0882:
Neural Network Models for Optical Computing
Ravindra A. Athale; Joel Davis, Editor(s)

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