Analysis of Head Pose Accuracy in Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
Predicting and estimating the accuracy of n-occular optical tracking systems
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
On fiducial target registration error in the presence of anisotropic noise
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Online estimation of the target registration error for n-ocular optical tracking systems
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Estimation of target registration error (TRE), a common measure of the registration accuracy, is an important issue in computer assisted surgeries. Within the last decade, several new approaches have been developed to estimate either the mean squared value of TRE or the distribution of TRE under different noise conditions. In this paper, we theoretically demonstrate that all the proposed algorithms converge to a general Maximum Likelihood (ML) solution. Numerical simulations are performed to validate our derivations. Using experimentally measured fiducial localization error, we provide an example of TRE prediction in the presence of anisotropic noise.