26 March 2008 Robust registration for change detection
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We address the problem of intra-subject registration for change detection. The goal is to separate stationary and changing subsets to be able to robustly perform rigid registration on the stationary subsets and thus improve the subsequent change detection. An iterative approach using a hybrid of parametric and non-parametric statistics is presented. The method uses non-parametric clustering and large scale hypothesis testing with estimation of the empirical null hypothesis. The method is successfully applied to 3D surface scans of human ear impressions containing true changes as well as data with synthesized changes. It is shown that the method improves registration and is capable of reducing the difference between registration using different norms.
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Sune Darkner, Dan Witzner Hansen, Rasmus R. Paulsen, Rasmus Larsen, "Robust registration for change detection", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69142T (26 March 2008); doi: 10.1117/12.770106; https://doi.org/10.1117/12.770106

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