Joint Transform Correlation with a Synthetic Discrimination Function for Object Classification

A novel method using adaptive progressive thresholding (APT) and shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) that employs a synthetic discriminant function (SDF) image for object classification is presented in this thesis. An adaptive progressive thresholding scheme first processes the given input scene to separate any objects present in the scene from the background. The preprocessing step results in an image having only the foreground objects, such as boats, trees and other cluttered regions, and hence reduces the search space for the correlation step significantly. The processed irμage is then fed to the shifted phase-encoded fringe-adjusted joint transform correlator, which produces a single and delta-like correlation peak for a potential target present in the input scene. A post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. The performance of the proposed technique is evaluated with images of several boats in different orientations. Simulation results show that the proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in an input scene. Research work is in progress to modify the classification technique in order to be applicable to nighttime images captured using an infra-red (IR) camera.