Computational models for search and discrimination

We present an experimental framework for evaluating metrics for the search and discrimination of a natural texture pattern from its background. Such metrics could help identify preattentive cues and un- derlying models of search and discrimination, and evaluate and design camouflage patterns and automatic target recognition systems. Human observers were asked to view image stimuli consisting of various target patterns embedded within various background patterns. These psycho- physical experiments provided a quantitative basis for comparison of human judgments to the computed values of target distinctness metrics. Two different experimental methodologies were utilized. The first meth- odology consisted of paired comparisons of a set of stimuli containing targets in a fixed location known to the observers. The observers were asked to judge the relative target distinctness for each pair of stimuli. The second methodology involved stimuli in which the targets were placed in random locations unknown to the observer. The observers were asked to search each image scene and identify suspected target locations. Using a prototype eye tracking testbed, the integrated testbed for eye move- ment studies, the observers' fixation points during the experiment were recorded and analyzed. For both experiments, the level of correlation with the psychophysical data was used as the basis for evaluating target distinctness metrics. Overall, of the set of target distinctness metrics considered, a metric based on a model of image texture was the most