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Proceedings Paper

Hierarchical neural architecture for visual pattern recognition and reconstruction
Author(s): Jagath C. Rajapakse; Raj S. Acharya
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Paper Abstract

A hierarchical self-organizing neural network which can recognize and reconstruct the traces of the previously learned binary patterns is presented. The recognition and reconstruction properties of the network are invariant with respect to distortion, noise, translation, scaling and partial rotation of the original training patterns. If two or more patterns are presented simultaneously, the network pays attention to each pattern selectively. The network can incorporate new training patterns for recognition without loosing its previously learned information. We demonstrate the usefulness of the network in image recognition, reconstruction and segmentation with simulation results.

Paper Details

Date Published: 1 July 1990
PDF: 9 pages
Proc. SPIE 1246, Parallel Architectures for Image Processing, (1 July 1990); doi: 10.1117/12.19588
Show Author Affiliations
Jagath C. Rajapakse, SUNY/Buffalo (United States)
Raj S. Acharya, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 1246:
Parallel Architectures for Image Processing
Joydeep Ghosh; Colin G. Harrison, Editor(s)

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