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Proceedings Paper

Feature extraction technique based on Hopfield neural network and joint transform correlation
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Paper Abstract

In this paper, a new Hopfield neural network based supervised filtering technique is proposed. The learnable filtering architecture has been developed by modifying the Hopfield network structure using 2D convolution instead of weight-matrix multiplications. This feature offers high speed learning and testing possibility for image feature extraction process. The learning property of the filtering technique is provided by using a recurrent learning algorithm. The proposed technique has been implemented using joint transform correlator. The requirement of non-negative data for optoelectronic implementation is provided by incorporating bias technique to convert the negative data to non-negative data. Simulation results for the proposed technique are reported for feature extraction problems such as edge detection, and vertical line extraction.

Paper Details

Date Published: 22 October 2004
PDF: 6 pages
Proc. SPIE 5557, Optical Information Systems II, (22 October 2004); doi: 10.1117/12.559753
Show Author Affiliations
Abdullah Bal, Univ. of South Alabama (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 5557:
Optical Information Systems II
Bahram Javidi; Demetri Psaltis, Editor(s)

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