dc.contributor.author | Zollei, Lilla | |
dc.contributor.author | Fisher, John | |
dc.contributor.author | Wells, William | |
dc.date.accessioned | 2005-12-22T01:30:39Z | |
dc.date.available | 2005-12-22T01:30:39Z | |
dc.date.issued | 2004-04-28 | |
dc.identifier.other | MIT-CSAIL-TR-2004-026 | |
dc.identifier.other | AIM-2004-011 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/30466 | |
dc.description.abstract | We formulate and interpret several multi-modal registration methods inthe context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptionsof each method yielding a better understanding of their relativestrengths and weaknesses. Additionally, we discuss a generativestatistical model from which we derive a novel analysis tool, the"auto-information function", as a means of assessing and exploiting thecommon spatial dependencies inherent in multi-modal imagery. Weanalytically derive useful properties of the "auto-information" aswell as verify them empirically on multi-modal imagery. Among theuseful aspects of the "auto-information function" is that it canbe computed from imaging modalities independently and it allows one todecompose the search space of registration problems. | |
dc.format.extent | 21 p. | |
dc.format.extent | 17309765 bytes | |
dc.format.extent | 765629 bytes | |
dc.format.mimetype | application/postscript | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.relation.ispartofseries | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory | |
dc.subject | AI | |
dc.subject | registration | |
dc.subject | information theory | |
dc.subject | unified framework | |
dc.title | A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration | |