Paper Info
Title
Fuzzy Correspondences And Kernel Density Estimation For Contaminated Point Set Registration
Abstract
Point set registration problem is challenging to solve in the presence of outliers. In this paper, we proposed a registration method based on fuzzy correspondences and kernel density estimation. The main idea of our method is that the moving point set consists of inliers represented using a mixture of Gaussian, and outliers represented via an additional uniform distribution, then we use the fuzzy correspondences to estimate the Gaussian elements in the mixture model. There are four parts of the paper: we formulate the contaminated point set registration problem as a mixture model according to the well known Gaussian mixture model (GMM) based method firstly. Secondly, Gaussian elements are estimated by fuzzy correspondences to increase the registration accuracy efficiently. Thirdly, the optimal transformation between two contaminated point sets is expressed by representation theorem, and solved by EM algorithm iteratively. Finally, we compare our proposed method with several state-of-the-art methods, and the results show that our method gets better performances than the other methods in most tested scenarios.
Year
DOI
Venue
2015
10.1109/SMC.2015.338
2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS
Keywords
Field
DocType
Point set registration, Fuzzy correspondence, Kernel density estimation, Mixture model, Regularization
Point set registration,Pattern recognition,Expectation–maximization algorithm,Computer science,Fuzzy logic,Outlier,Gaussian,Artificial intelligence,Variable kernel density estimation,Mixture model,Kernel density estimation
Conference
ISSN
Citations 
PageRank 
1062-922X
3
0.37
References 
Authors
14
5
Name
Order
Citations
PageRank
Gang Wang1927.17
Zhicheng Wang217617.00
Yufei Chen332233.06
weidong zhao47714.73
Xianhui Liu5141.75