Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82124
Title: An effective membership probability representation for point set registration
Authors: Fang, LJ
Sun, ZL
Lam, KM 
Issue Date: 2020
Source: IEEE access, 2020, v. 8, 8952707, p. 9347-9357
Abstract: How to design an effective membership probability is an important component for Gaussian mixture model (GMM) of point set registration. In order to improve the robustness of point set registration, in this paper, a new representation is proposed for membership probability of Gaussian mixture model, by utilizing two types of feature descriptor, i.e. shape context or fast point feature histograms. Moreover, for each point of the model point set, a dynamic programming (DP) algorithm is developed to search for the optimal candidate points from the target point set. Compared to the state-of-the-art approaches, the proposed approach is more robust to deformation, outlier, occlusion, and rotation. Experimental results on several widely used 2D and 3D data demonstrate the effectiveness and feasibility of the proposed algorithm.
Keywords: Dynamic programming
Gaussian mixture model
Point set registration
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2964840
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication L. Fang, Z. Sun and K. Lam, "An Effective Membership Probability Representation for Point Set Registration," in IEEE Access, vol. 8, pp. 9347-9357, 2020, is available at https://doi.org/10.1109/ACCESS.2020.2964840
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