Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Automated 3D registration of truncated MR and CT images of the head
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Interpolation artefacts in mutual information-based image registration
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Mutual Information for Lucas-Kanade Tracking (MILK): An Inverse Compositional Formulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental, scalable tracking of objects inter camera
Computer Vision and Image Understanding
Adaptive Grid Generation Based Non-rigid Image Registration using Mutual Information for Breast MRI
Journal of Signal Processing Systems
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Optimal detection and tracking of feature points using mutual information
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Using mutual information for appearance-based visual path following
Robotics and Autonomous Systems
Direct model based visual tracking and pose estimation using mutual information
Image and Vision Computing
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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.