Analyzing target recognition issues using the informational difference concept

Abstract

A model for analyzing issues involving monospectral target recognition is presented. These issues include modeling target detection, recognition and identification thresholds, and predicting the functional parametric dependencies of the results of observation experiments by human observers. The model makes extensive use of concepts used in Information Theory. An image of a certain scene is treated as a sample of an entire set of images of that particular scene. A difference measure, called the Informational Difference (InDif) between two image sets is defined. The main assertion is that accomplishing target recognition tasks is equivalent to setting thresholds for the InDif. The applicability of the InDif to the performance of the Human Visual System (HVS) is shown both analytically, in very simple situations, and in computer calculations involving noisy images. Finally, a single framework for dealing with the HVS and Artificial Intelligence systems in target recognition applications is shown to result naturally from the InDif formalism.