A Unifying Framework for Mutual Information Methods for Use (2006)
| Venue: | in Non-Linear Optimisation,” Proc. Ninth European Conf. Computer Vision |
| Citations: | 9 - 2 self |
BibTeX
@INPROCEEDINGS{Dowson06aunifying,
author = {Nicholas Dowson and Richard Bowden},
title = {A Unifying Framework for Mutual Information Methods for Use},
booktitle = {in Non-Linear Optimisation,” Proc. Ninth European Conf. Computer Vision},
year = {2006},
pages = {365--378}
}
OpenURL
Abstract
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-byside 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. 1







