A Computational Model of Human Perception - Application to Target Detection

M-Vision Inc., along with the University of Michigan and Michigan State University, proposes to develop a new and novel solution to the problem of target detection and discrimination in a complex scene. This solution is obtained by innovatively combining Gabor wavelets and scale space inference. The result is a computational model of human perception that resembles the human visual system in its information gathering stages, its cognitive or information processing stages, and the manner in which these two stages interact with each other. At the heart of this model is a mechanism to learn and reason about images much like humans do. This is achieved by building a knowledge base about image features and constructing a Fuzzy rule book to map observed image features onto a target-background detectability metric. As a consequence, every image is interpreted in a dynamic fashion - features that are significant for that particular image are first obtained and then the importance of these for target-background detectability is assessed. In Phase I, a feasibility study is proposed, including the design of appropriate experiments and the establishment of a protocol to assess performance. The deliverables at the end of Phase I include the new Gabor wavelet based target signature metric, the associated human cognition based scale space inference engine, and the entire set of experiments conducted and results obtained. The proposed computational model of human perception has wide spread potential use among Department of Defense scientists and engineers in their survivability, signature analysis, and sensor characterization studies. Furthermore, the model also has much potential use among scientists and engineers in commercial automobile and other automotive-related industries in their conspicuity analysis, collision avoidance, all-weather driving assistance, and realistic driving assistance, and realistic driving simulators studies.