Published March 1988
| Published
Journal Article
Open
Computing motion using analog and binary resistive networks
Chicago
Abstract
The authors describe recent developments in the theory of early vision that led 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. The optical flow is computed by injecting currents into resistive networks and recording the resulting stationary voltage distribution at each node. The authors believe that these networks, which they implemented in complementary metal-oxide-semiconductor (CMOS) very-large-scale integrated (VLSI) circuits, represent plausible candidates for biological vision systems.
Additional Information
© 1988 IEEE. An early version of this model was developed and implemented in collaboration with A.L. Yuille (1987). Mathew Avalos and Andrew Hsu wrote the code for the Imaging Technology system, and Erik Staats for the Ncube. Koch is supported by an Office of Naval Research Young Investigator Award, grants from the NSF Advanced Engineering Program (EET-87 14710 and IST-8700064), a grant from the Dept. of Energy (DE-FG03-85 ER25009), and the James Lee Powell Foundation. Mead is also supported by the Office of Naval Research and the System Development Foundation. A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, and was sponsored by NASA.Attached Files
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Additional details
- Eprint ID
- 52992
- Resolver ID
- CaltechAUTHORS:20141217-153250575
- Office of Naval Research (ONR)
- NSF
- EET-8714710
- NSF
- IST-8700064
- Department of Energy (DOE)
- DE-FG03-85 ER25009
- James Lee Powell Foundation
- System Development Foundation
- NASA/JPL/Caltech
- Created
-
2014-12-17Created from EPrint's datestamp field
- Updated
-
2023-04-18Created from EPrint's last_modified field
- Caltech groups
- Koch Laboratory (KLAB)