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.
Highlights
Tte task of point set registration is to find an optimal spatial transformation to align a model point set and a target point set
For each point of the model point set, a dynamic programming algorithm is developed to search for the optimal candidate points from the target point set
EXPERIMENTAL DATA AND SET-UP To evaluate the effectiveness of the proposed dynamic programming based membership probability method, denoted as DP-MP, we present the experimental comparisons to six state-of-the-art algorithms, including MSTT [20], asymmetric point matching (APM) [9], coherent point drift (CPD) [16], PRGLS [17], SCGF [21] and MR [22], [23]
Summary
Tte task of point set registration is to find an optimal spatial transformation to align a model point set and a target point set. In [17], the correspondences between two point sets were obtained by matching their feature descriptors, and used to initialize the membership probabilities according to two rules.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
