Image registration based on kernel-predictability
Computer Vision and Image Understanding
PCA based regional mutual information for robust medical image registration
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Kernel-predictability: a new information measure and its application to image registration
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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A new method for updating the template in a feature tracking application is presented, which has minimal memory and processing overhead. The proposed method is an expectation maximisation inspired approach based on modelling the variable appearance of a template using a Gaussian mixture model in a discrete metric space, termed the M^3 I tracker for short.The proposed technique is compared to various other techniques in several experiments, where it performs robustly. Several comparison methods are outperformed. In addition to robust template tracking it has wider applications to advanced techniques such as AAMs and deformable templates.