This paper describes a computationally efficient method for boosting the performance of correlation filters. The correlation filter is augmented with an array of histograms and the associated affinity numbers, obtained from the training image set utilized in construction of the filter. In the operational phase, histograms of sensor images are examined only at the image neighborhoods where the correlation filter provides initial indications of a target occurrence. The presence of a target is affirmed at image locations that pass both the peak cross correlation and histogram tests. Results of numerous experiments demonstrate that reinforcement of the spatial correlation filter with the trainer histograms leads to more robust Fourier filters for target detection and classification.
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