Nonlinear cascaded correlation processes to improve the performances of automatic spatial-frequency-selective filters in pattern recognition.

A recognition process consisting of two cascaded correlation stages with a sigmoid nonlinearity applied in the first correlation plane is investigated. The filters are computed to give prespecified central correlation amplitudes in the second correlation plane when inputs are reference images. It is also desired that the second correlation amplitudes with the training images should minimize the cost function of the automatic spatial-frequency selection algorithm to reduce distortion sensitivity and to improve the performance of the filters. Filter computation methods are given, and it is shown why two such correlation processes may improve the correlation performance. Numerical simulations are described and compared with the one-stage correlation system that works with the automatic spatial-frequency selection filter.