A Hierarchical Bayesian Model for Matching Unlabeled Point Sets

Hu, Xin and Zhang, Xiaodong and Zhou, Xuequan and Zhang, Hua and Li, Chunshan and Ding, Deqiong and Chu, Dianhui (2019) A Hierarchical Bayesian Model for Matching Unlabeled Point Sets. In: Mobimedia 2019, 29-30 Oct 2019, Wehai, China.

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Point set registration is the key in many scientific disciplines. Target at several challenges in registration (e.g. initial registration, outliers, missing data, and local trap), we propose a robust registration method for two point sets using a hierarchical Bayesian model, which is combined with Markov chain Monte Carlo inference. Our approach is based on the introduction of a template of hidden locations underlying the observed configuration points. A Poisson process prior is assigned to these locations, resulting in a simplified formulation of the model. We make use of a structure containing the relevant information on the matches. We conduct several experiments to demonstrate that our algorithm is accurate and robust.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: hierarchical model markov chain monte carlo matching registration
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor I.
Date Deposited: 09 Sep 2020 06:59
Last Modified: 09 Sep 2020 06:59
URI: https://eprints.eudl.eu/id/eprint/83

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