Abstract
An optoelectronic neural network based detection technique is proposed for multi-class distortion-invariant pattern recognition. The neural network is utilized in the training stage for a sequence of multi-class binary and gray level images for supervised learning using shifted phase-encoded joint transform correlator with fringe adjusted filter in the hidden layer to create composite images that are invariant to distortion. Simulation results show that the proposed technique is efficient in recognizing targets in variable environmental conditions.