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Registration errors for registering a source shape containing outliers to a mesh target under large misalignment. (Experiment 3B).

By Seth D. Billings (679283), Emad M. Boctor (648065) and Russell H. Taylor (679284)


<p>Source shapes were randomly generated from the hip mesh (<a href="" target="_blank">Fig. 1A</a>), misaligned by [30, 60] mm / degrees, and registered back to the mesh. The test cases represent the different noise models used to generate noise on the source shape (<a href="" target="_blank">Table 4</a>). Outliers were added to the source shape constituting (A): 5%, (B): 10%, (C): 20%, and (D): 30% of the source points. For each test case, 300 randomized trials were conducted, with successful registrations being used to compute an average target registration error (TRE). The error bars provide approximate standard deviations of the reported average TRE values. The proposed IMLP algorithm was evaluated relative to standard ICP [<a href="" target="_blank">1</a>] and relative to a robust variant of ICP [<a href="" target="_blank">4</a>].</p

Topics: Biological Sciences, cpd, correspondence, Iterative Closest Point, GICP, modeling measurement noise, Coherent Point Drift, Optimal Shape Alignment, pd, noise model, IMLP, anisotropic registration methods, novel search strategy, accuracy, icp, GTLS, algorithm
Year: 2015
DOI identifier: 10.1371/journal.pone.0117688.g004
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Provided by: FigShare
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