UM
Joint Registration of Multiple Point Sets by Preserving Global and Local Structure
Zhu H.2; Guo B.2; Yuen K.-V.1; Leung H.3; Li Y.2; Tian Z.2
2018-09-05
Source Publication2018 21st International Conference on Information Fusion, FUSION 2018
Pages1459-1463
AbstractIn previous work on joint multiple point sets registration, the multiple point sets are often formulated by a Gaussian mixture model (GMM) and the registration is then cast to a clustering problem, which aims to exploit global relationships on the multiple point sets. However, local relationships on the multiple point sets are ignored in the state-of-the-art joint multiple point sets registration techniques. In this paper, the multiple point sets are assumed to be generated from a GMM. Local features of the multiple point sets, such as shape context, are proposed to infer the membership probabilities of the GMM. The problem of joint multiple point sets registration can be performed by maximum likelihood of the GMM. The parameters of GMM and registration are estimated by an expectation maximization algorithm. Comprehensive experiments demonstrate that our proposed method has better performance than the state-of-the-art methods.
Keywordexpectation maximization Gaussian mixture model local features multiple point sets registration
DOI10.23919/ICIF.2018.8455662
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Chongqing University of Posts and Telecommunications
3.University of Calgary
Recommended Citation
GB/T 7714
Zhu H.,Guo B.,Yuen K.-V.,et al. Joint Registration of Multiple Point Sets by Preserving Global and Local Structure[C],2018:1459-1463.
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