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

摘要

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.