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Proceedings Paper

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR
Author(s): Andrew Baek; Nabil H. Farhat
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Paper Abstract

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Paper Details

Date Published: 14 October 1998
PDF: 12 pages
Proc. SPIE 3462, Radar Processing, Technology, and Applications III, (14 October 1998); doi: 10.1117/12.326749
Show Author Affiliations
Andrew Baek, Univ. of Pennsylvania (United States)
Nabil H. Farhat, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 3462:
Radar Processing, Technology, and Applications III
William J. Miceli, Editor(s)

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