This research will develop a framework for the rapid detection, location, and identification of visual targets in unconstrained real world domains. This framework will lead to algorithms which can be implemented on portable computers with video input with the goal of being used, for example, to enable the blind/visually impaired to navigate in real world scenes. These requirements mean that the algorithms must be extremely efficient at extracting information from the input images. The approach will use statistical analysis of the targets and background, taking into account variations due to illumination and viewpoint variations, to determine probabilistic models for the appearance of the target and background. From these models, sets of tests and groups of tests will be determined. These tests will be designed to be maximally informative, based on statistical measures of errors rates such as Chernoff Information, and to lead to fast implementations on portable PC's. The search strategy is based on the intuition of picking tests which maximize the expected gain in information about the target hypothesis. In practical problems, however, it will not always be possible to compute these expected information gains in real time. Therefore the search strategy will make use of a more general formulation in terms of A algorithms where the search is guided by a heuristics taylored to the application domain. Expected information is one possible heuristic but there are many others which are more easily computable and which can still give provable convergence to the optimal solution.