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.
Official URL: http://dx.doi.org/10.1007/11744023_29
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|
|Deposited By:||Symplectic Elements|
|Deposited On:||12 Jun 2012 09:34|
|Last Modified:||13 Apr 2013 14:47|
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