Spatial-domain implementation of optimal multicriteria correlation filters

We show that optimal regions of support for correlation filters in the frequency domain can be approximated by relatively small convolution kernels in the spatial domain. We present an optimal approach for generating regions of support, as well as a fast nonoptimal approach for conventional optical correlators. Because the convolution kernels are similar to low-pass filters, the resulting input image to a correlator is always positive valued. We show that the performance of the convolution-based approach is comparable with the optimal frequency-domain approach. An important advantage of our method is that it can be implemented on low-cost arithmetic frame grabbers that can perform convolution with small kernels in real time. In addition, our method can be used in conjunction with a filter spatial light modulator that cannot produce a zero state.