Multi-resolution approach to automatic target recognition with a hybrid evolutionary algorithm and response analysis

Abstract

A multi-resolution approach to automatic target recognition is described that employs a hybrid evolutionary algorithm (HEA) and image transform in a form of image local response. Given images of the targeted area (TA) and the targeted object (TO) located in TA, the proposed method repeatedly applies cross-correlation on different resolution levels (zooming in), in order to find the area TA and the object TO in the large-scale image of the region of interest (ROI). Both images of ROI and TA undergo peculiar transformation called image local response. Given geometric transformation T(V) of the images under specified parameter vector V, image local response is defined as an image transform R(V) that maps an image into itself, with the small perturbation of the parameter vector V. Unit variations of the components of the parameter vector V are applied to the image, and the corresponding variations of the least squared difference of the gray levels of the two images (i.e., before and after the parameter variation) form an image response matrix M(V). Cross-correlation of the response matrices built for ROI and TA outlines a potential range of resolutions of the TA. A hybrid Evolutionary algorithm can be applied then, in order to find the correct parameters V for TA with the reference to ROI.