Proceedings of Ground Target Modeling and Validation ’99, Houghton, MI (August 1999)
The overall objective of this project is to develop a computational model for predicting probability of detection during search for hard-to-see targets. This model is image based: it uses imagery for input, rather than estimated parameter values characterizing critical factors such as clutter and target detectability. Consequently, it generates probability-of-detection values that are functions of image content, rather than functions of subjectively estimated parameters. The input domain is infrared or visible-light imagery of distant vehicle targets in cluttered scenes. Such hard-to-see targets are generally only detected once they have been fixated. Hence, our modeling approach focuses primarily on factors influencing the choice of fixation points during visual search. A saliency map is constructed from bottom-up image features, such as local contrast. To account for top-down cognitive effects—such as bias towards the horizon—a separate cognitive bias map is generated. The combination of these two maps provides a Fixation Probability Map (FPM). Given the FPM, a sequence of fixation points is generated in a way that accounts for imperfect memory of past fixation locations. Results are presented comparing model-generated FPM’s with eye-tracker data collected from observers in visual search experiments.
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