Distortion-invariant color pattern recognition using multiple phase-shifted-reference-based joint transform correlation incorporating synthetic discriminant function

Abstract

This paper proposes a new pattern recognition system employing optical joint transform correlation (JTC) technique which offers a great number of advantages over similar digital techniques, including very fast operation, simple architecture and capability of updating the reference image in real time. The proposed JTC technique incorporates a synthetic discriminant function (SDF) of the target image estimated from different training images to make the pattern recognition performance invariant to noise and distortion. It then involves four different phase-shifted versions of the same target SDF reference image, which are individually joint transform correlated with the given input scene. When the correlation signals are combined, it produces a single cross-correlation peak corresponding to each potential target present in the given input scene. The proposed technique also includes a fringe-adjusted filter to generate a delta-like correlation peak with high discrimination between the target and the background noise. The pattern recognition performance is further enhanced by incorporating the color information of the target objects in the proposed technique. The proposed technique is investigated using computer simulation where it shows efficient and successful target detection performance in different complex environments.