Engineers and scientists at the US Army's Night Vision and Electronic Sensors Directorate (NVESD) are in the process of evaluating the German CAMAELEON model, a signature evaluation model that was created for use in designing and evaluating camouflage in the visible spectrum and is based on computational vision methodologies. Verification and preliminary validation have been very positive. For this reason, NVESD has planned and is currently in the early execution phase of a more elaborate validation effort using data from an Army field exercise known as DISSTAF-II. The field exercise involved tank gunners, using the currently fielded M1 Abrams tank sights to search for, to target, and to `fire on' (i.e. to pull the trigger to mark target location) a variety of foreign and domestic vehicles in realistic scenarios. Data from this field exercise will be combined with results of a laboratory measurement of perceptual target detectabilities. The purpose of the laboratory measurement is to separate modeled effects from unmodeled effects in the field data. In the laboratory, observers will be performing a task as similar as possible to that modeled by CAMAELEON. An important feature of this data is that the observers will know where the target is located and will rate the detectability of the targets in a paired comparison experiment utilizing the X-based perceptual experiment testbed developed at the University of Tennessee. For the laboratory measurement the subjects will view exactly the same images as those to be analyzed by CAMAELEON. Three correlations that will be found are expected to be especially important. The correlation between perceptual detectability and model predictions will show the accuracy with which the model predicts human performance of the modeled task (rating target detectabilities). The correlation between laboratory and field data will show how well perceived detectability predicts tank gunner target detection in a realistic scenario. Finally, the correlation between model predictions and detection probabilities will show the extent to which the model can actually predict human field performance.