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Proceedings Paper

Efficiency in the Generation of Hierarchical Feature Detectors in Neural Nets
Author(s): Oleg G. Jakubowicz
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Paper Abstract

Biologically in the primary visual area in the brain there is a full set of elementary feature detectors at every location in the retinal field. These detectors are akin to the two dimensional edge and bar detectors commonly used in computer vision. We present in this paper biological simulation details of how these particular detectors most probably might be neurobiologically constructed and organized into a topologically ordered output plane. Then we point out the pitfall of over-representation of information that can occur in naive self-organizing neural network models for vision and present. how our properly constructed network overcomes this problem. This paper is intended to give some productive guidelines for constructing self-organizing networks whose cells have locally receptive fields.

Paper Details

Date Published: 1 March 1990
PDF: 5 pages
Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.969996
Show Author Affiliations
Oleg G. Jakubowicz, State University of New York at Buffalo (United States)


Published in SPIE Proceedings Vol. 1198:
Sensor Fusion II: Human and Machine Strategies
Paul S. Schenker, Editor(s)

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