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.

Distortion tolerant correlation filter design.

This paper introduces a computationally efficient algorithm for synthesis of a distortion tolerant correlation filter and associated threshold, denoted collectively as the enhanced matched filter (EMF). Application areas of EMF include imagery based automatic target detection and recognition and biometrics. The EMF is synthesized from a set of training images characterizing the target of interest within the expected distortion range.

Discrimination among similar looking noisy color patches using Margin Setting.

The goal of discrimination of one color from many other similar-appearing colors even when the colored objects show substantial variation or noise is of obvious import. We show how to accomplish that using a technique called Margin Setting. It is possible not only to have very low error rates but also to have some control over the types of errors that do occur.

Application of supergeneralized matched filters to target classification.

The matched filter (MF) is the optimum linear operator for distinguishing between a fixed signal and noise, given the noise statistics. A generalized matched filter (GMF) is a linear filter that can handle the more difficult problem of a multiple-example signal set, and it reduces to a MF when the signal set has only one member. A supergeneralized matched filter (SGMF) is a set of GMFs and a procedure to combine their results nonlinearly to handle the multisignal problem even better.