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Multiple Image Recognition By Nonlinear Optical Correlation

We investigate the performance of a recently introduced bipolar joint transform image correlator when multiple reference objects and single and multiple targets are present at the input plane. The bipolar joint transform correlator uses nonlinearity at the Fourier plane to threshold the Fourier transform interference intensity to only two values, 1 and -1. The performance of the bipolar correlator is compared to the classical joint transform image correlator when multiple objects are present at the input plane. We show that the performance of the bipolar correlator is substantially superior to that of the classical joint transform image correlator in the areas of autocorrelation peak intensity, autocorrelation peak to sidelobe ratio, autocorrelation bandwidth, and discrimination sensitivity. It is shown that when multiple objects are present at the input plane, the classical joint transform image correlator produces large cross-correlation signals and poorly defined autocorrelation signals with large bandwidth. The large cross-correlation signals are comparable in intensity with the autocorrelation peak and can falsely indicate the presence of many targets. On the other hand, the bipolar joint transform image correlator produces well-defined autocorrelation signals with very low cross-correlation level. Computer simulations are used to test both types of correlators, and 3-D plots of the correlation functions are provided and discussed.