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Proceedings Paper

Image recognition and learning in parallel networks
Author(s): Raghu Raghavan; Frank W. Adams; H. T. Nguyen; Joseph Slawny
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Paper Abstract

We describe the design of an image-recognition system and its performance on multi-sensor imagery. The system satisfies a list of natural requirements, which includes locality of inferences (for efficient VLSI implementation), incorporation of prior knowledge, multi-level hierarchies, and iterative improvement. Two of the most important new features are: a uniform parallel architecture for low-, mid- and high- level vision; and achievement of recognition through short-, as opposed to its long-time behavior, of a dynamical system. Robustness depends on collective effects rather than high precision of the processing elements. The resulting network displays a balance of high speed and small size. We also indicate how this architecture is related to the Dempster-Shafer calculus for combining evidence from multiple sources, and present novel methods of learning in such networks, including one that addresses the integration of model-based and data-driven approaches.

Paper Details

Date Published: 1 July 1990
PDF: 16 pages
Proc. SPIE 1247, Nonlinear Image Processing, (1 July 1990); doi: 10.1117/12.19615
Show Author Affiliations
Raghu Raghavan, Lockheed Palo Alto Research Lab. (United States)
Frank W. Adams, Lockheed Palo Alto Research Lab. (United States)
H. T. Nguyen, Lockheed Palo Alto Research Lab. (United States)
Joseph Slawny, Virginia Polytechnic Institute and State Univ. (United States)

Published in SPIE Proceedings Vol. 1247:
Nonlinear Image Processing
Edward J. Delp, Editor(s)

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