Bayesian Point Set Registration

Adam Spannaus, Vasileios Maroulas, David J Keffer, Kody Law

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    Point set registration involves identifying a smooth invertible transformation
    between corresponding points in two point sets, one of which may be smaller
    than the other and possibly corrupted by observation noise. This problem is traditionally decomposed into two separate optimization problems: (i) assignment or correspondence, and (ii) identification of the optimal transformation between the ordered point sets. In this work, we propose an approach solving both problems simultaneously. In particular, a coherent Bayesian formulation of the problem results in a marginal posterior distribution on the transformation, which is explored within a Markov chain Monte Carlo scheme. Motivated by Atomic Probe Tomography (APT), in the context of structure inference for high entropy alloys (HEA), we focus on the registration of noisy sparse observations of rigid transformations of a known reference configuration. Lastly, we test our method on synthetic data sets.
    Original languageEnglish
    Title of host publicationProceedings of MATRIX Conference on Computational Inverse Problems
    ISBN (Electronic)978-3-030-04161-8
    Publication statusPublished - 14 Mar 2019


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