An effective membership probability representation for point set registration

Lu Jie Fang, Zhan Li Sun, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number8952707
Pages (from-to)9347-9357
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 8 Jan 2020

Keywords

  • dynamic programming
  • Gaussian mixture model
  • point set registration

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this