A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation

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...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Nicholas Dowson, Richard Bowden
• Keywords: Computer Vision, ECCV 2006, In-Parzen Windowing, MI Variants, Non-linear Intensity Relationships, Post-parzen Windowing, Standard Sampling