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Proceedings Paper

Intelligent vision systems using dynamic neural networks
Author(s): Natalie Clark; Paul Furth
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Paper Abstract

The design of a silicon eye using dynamical neural networks. Our silicon eye is capable of not only able to compensate for defocus, but it can also compensate for other aberrations including astigmatism, coma, and spherical aberrations. In addition, the silicon retina acts as a reconfigurable dynamic neural network to enable real-time image processing. The silicon eye uses three key enabling technologies. First, high-speed active pixel photo-diodes are used as photo-detectors for both imaging and for wavefront sensing. The design of the active pixel photo- detectors is described along with experimental results characterizing their performance. Second, the analog signals received from the photo-detectors and processed by the active pixel circuitry is fed into a smart vision chip. The smart vision chip is a reconfigurable neural network capable of real-time reconstruction of the phase information associated with the imaging system. The micro mirrors are active optic devices that can be used to compensate for optical aberrations. Experimental results obtained from the circuit implementation of the dynamical network networks are presented. The experimental results obtained from our intelligent vision system demonstrate that dynamical neural networks offer advantages in speed, cost, size, and power consumption.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343031
Show Author Affiliations
Natalie Clark, Air Force Research Lab. (United States)
Paul Furth, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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