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Proceedings Paper

Frequency-based pattern recognition using neural networks
Author(s): Simon Wenfeng Lu
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Paper Abstract

A pattern recognition algorithm is proposed, in frequency domain, using a backpropagation neural network. The algorithm extracts distinct frequency features from reference patterns and compares them with the corresponding features of an unknown pattern. The feature sets are learned and recognized through backpropagation neural networks. Multiple neural networks are used to form a classification network. Since the feature set extracted is significantly smaller than the pattern image, the neural network is fast and accurate. Preliminary results indicates that certain features in frequency domain remain consistent for images of the same pattern and it is possible to extract these features for pattern recognition. Experimental results have also indicated that these features can be used to distinguish one pattern from another accurately. The system presented herein exhibits the advantages over previous systems of increasing recognition accuracy, i.e., lower false identification rate, increasing recognition speed, and decreasing data storage space. A method to dynamically identify frequency features in any set of reference patterns and to classify an unknown feature set using neural network is described in detail.

Paper Details

Date Published: 1 October 1991
PDF: 11 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48400
Show Author Affiliations
Simon Wenfeng Lu, Hyper-L, Inc. (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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