A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.