Efficient pattern recognition and classification using a combination of maximum average correlation height and polynomial distance classifier correlation filters

Abstract

A new technique using a combination of maximum average correlation height (MACH) filter and polynomial distance classifier correlation filter (PDCCF) for simultaneous detection and classification of single/multiple identical and dissimilar targets is proposed in this paper. In this technique, a MACH filter is formulated for each desired target class from the training images of the corresponding target with expected size and orientation variations such that the size of the filter is the same as the input scene. Then a multi-class PDCCF is formulated from the training images of all target classes such that the size of the filter is the same as the expected targets. For real time applications, the input scene is first correlated with all MACH filters and the correlation outputs are combined. The regions of interest (ROI) containing the probable targets are selected from the input scene based on the ROIs with higher correlation peak values in the combined correlation output. The PDCCF filter is then applied to these ROIs to identify target types and reject clutters and/or backgrounds. To increase the robustness of the proposed technique, multiple filters are formulated for multiple ranges of target size and/or orientation variations. This two-stage system is faster and yields more accurate results compared to the existing three-stage system, which involves wide area prescreening, detection using MACH filters, and classification using distance classifier correlation filter. The simulation results using real life imagery show that the proposed technique can detect and classify the desired targets with higher efficiency irrespective of their distortion or the number of targets present in the input scene, when compared to the alternate techniques.