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A unifying framework for mutual information methods for use in non-linear optimisation

Dowson, N and Bowden, R (2006) A unifying framework for mutual information methods for use in non-linear optimisation In: ECCV 2006, 2006-05-07 - 2006-05-13, Graz, Austria.

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Abstract

Many variants of MI exist in the literature. These vary primarily in how the joint histogram is populated. This paper places the four main variants of MI: Standard sampling, Partial Volume Estimation (PVE), In-Parzen Windowing and Post-Parzen Windowing into a single mathematical framework. Jacobians and Hessians are derived in each case. A particular contribution is that the non-linearities implicit to standard sampling and post-Parzen windowing are explicitly dealt with. These non-linearities are a barrier to their use in optimisation. Side-by-side comparison of the MI variants is made using eight diverse data-sets, considering computational expense and convergence. In the experiments, PVE was generally the best performer, although standard sampling often performed nearly as well (if a higher sample rate was used). The widely used sum of squared differences metric performed as well as MI unless large occlusions and non-linear intensity relationships occurred. The binaries and scripts used for testing are available online.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The original publication is available at http://www.springerlink.com
Uncontrolled Keywords: MULTIMODALITY IMAGE REGISTRATION, INTERPOLATION ARTIFACTS, MATHEMATICAL-THEORY, MAXIMIZATION, COMMUNICATION
Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Depositing User: Symplectic Elements
Date Deposited: 12 Jun 2012 08:34
Last Modified: 09 Jun 2014 13:18
URI: http://epubs.surrey.ac.uk/id/eprint/531447

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