Real Time Intelligent Target Detection and Analysis with Machine Vision

This paper presents an algorithm for detecting a specified set of target objects embedded in visual imagery for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and non-target objects located within a cluttered environment by evaluating 40x40 image blocks belonging to a segmented image scene. Using directed principal component analysis, the data dimensionality of an image block is first reduced and then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. Following clustering, each image pattern is fed into an associated trained neural network for classification. A detailed description of our algorithm will be given in this paper. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.