Distortion tolerant correlation filter design

Inherent limitation of digital imagery: spatial-phase vacillations and the ambiguity function

The spatial relationship of a scene image upon a pixilated focal-plane-array can temporally change due to scene or camera motion, microjitter of camera line-of-sight, etc. It was found that degradation caused by subpixel spatial-phase vacillations (SPV) upon the performance of matched filters was unexpectedly significant. Subpixel spatial-phase vacillations can cause degradations in matched filter performance to detect desired objects and discriminate other objects. SPV appear to be an inherent limitation of digital imagery when processed using matched filter methodology and can negatively impact the performance of systems. Mitigation of this degradation was found to be possible by utilizing one of several matched filter constructions such as a multi-filter enhanced matched filter (EMF) bank and a single-filter EMF. A significant conclusion of this investigation is that, for automatic target recognition applications, improved overall task performance should be realized by the use of an EMF bank. Dramatic reduction in computational resources for image matching multispectral imagery was accomplished by spectrally-collapsing multispectral imagery to form pseudoimages with the spectral information appearing as a texture in a grayscale image. The pattern recognition ambiguity function, which sets the fundamental performance limit of an image processing system, is introduced.

Fourier filter augmented with trainer histograms

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.