Verifying model-based alignments in the presence of uncertainty

This paper introduces a unified approach to the problem of verifying alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. Our approach is independent of whether the uncertainty is distributed or bounded, and, moreover, incorporates information about the domain in a formally correct manner. Information which can be incorporated includes the error model, the distribution of background features, and the positions of the data features near each predicted model feature. Experiments are described that demonstrate the improvement over previously used methods. Furthermore, our method is efficient in that the number of operations is on the order of the number of image features that lie nearby the predicted model features.