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

Abdullah Bal; Mohammad S. Alam

doi:10.1117/12.559753

22 October 2004 Feature extraction technique based on Hopfield neural network and joint transform correlation

Abdullah Bal, Mohammad S. Alam

Proceedings Volume 5557, Optical Information Systems II; (2004) https://doi.org/10.1117/12.559753
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

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.

Citation Download Citation

Abdullah Bal and Mohammad S. Alam "Feature extraction technique based on Hopfield neural network and joint transform correlation", Proc. SPIE 5557, Optical Information Systems II, (22 October 2004); https://doi.org/10.1117/12.559753

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