Cognition Models for Visual Target Discrimination

Computational vision models (CVM) of the pre-cognitive, retino-cortical "front end" stages of human vision have demonstrated successfully explained visual performance for simple visual tasks with simple stimuli. However, extending these models to predict or simulate human visual performance for complex involving target discrimination such as discriminating, a tank from a truck, have not been very successful. Discrimination tasks involve cognition. Objects are examined and categorized based on prior knowledge of target structures and features. Expectations regarding what objects are present and where they will be found also influence visual interpretation. Successful modeling of this cognitive "back end" of vision requires a model of the prior knowledge of the visual appearance and structure of the scene. The proposed Phase I project will develop and demonstrate a "back-end" cognition model for visual target discrimination, The model will use a structural and spatial decomposition of the target into component parts and features as the basis for the model target categories. The features will actually be composed of the spatial filters used by the human visual system. In Phase II this model will be reviewed, refined, fully automated, calibrated, published for peer review, and demonstrated for a variety of military and commercial applications. This technology will reduce the time and cost of military and civilian vehicle design involving (1) camouflage, concealment and deception technologies, (2) automotive conspicuity enhancements crash avoidance, (3) imaging electrooptical sensor Systems for both target acquisition and enhanced driver's vision, and (4) visual identification friend or foe (IFF).