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)
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing inverse compositional image alignment
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A unifying framework for mutual information methods for use in non-linear optimisation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Generalizing Inverse Compositional and ESM Image Alignment
International Journal of Computer Vision
Linear Regression and Adaptive Appearance Models for Fast Simultaneous Modelling and Tracking
International Journal of Computer Vision
Lucas-Kanade based entropy congealing for joint face alignment
Image and Vision Computing
Direct model based visual tracking and pose estimation using mutual information
Image and Vision Computing
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Mutual Information (MI) is popular for registration via function optimisation. This work proposes an inverse compositional formulation of MI for Levenberg-Marquardt optimisation. This yields a constant Hessian, which may be pre-computed. Speed improvements of 15% were obtained, with convergence accuracies similar to those of the standard formulation.