Robust, sensor-independent target detection and recognition based on computational models of human vision

Abstract

Most current artificial vision systems lack robustness and are applicable only to a narrow range of tasks. Crevier (1997) has suggested that this is due to their reliance on a small number of vision mechanisms and to lack of knowledge about how vision algorithms should be integrated. We suggest a systems approach to artificial vision based on computational vision research. The capabilities of biological vision systems are contrasted with those of current piecemeal approaches to artificial vision. A mature, comprehensive vision system, called the Georgia Tech Vision (GTV) simulation is described. GTV incorporates quasilinear filter mechanisms to simulate the processing performed by simple and complex cortical cells. The outputs of these mechanisms are adaptively combined to discriminate targets from clutter and/or one another. GTV outputs predictions of human search and detection performance and/or targeting metrics for automatic target recognition (ATR) applications. Studies validating GTV as a model of human search and detection performance and demonstrating its performance as an ATR are presented.