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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6738 |
| Date | 28 April 2004 |
| Creators | Zollei, Lilla, Fisher, John, Wells, William |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 21 p., 2760680 bytes, 531001 bytes, application/postscript, application/pdf |
| Relation | AIM-2004-011 |
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