XIANG Lin, QU Hanbing, TAO Haijun, WANG Jiaqiang. Variational Bayesian Approximation for Affine Point Set Matching[J]. Chinese Journal of Electronics, 2015, 24(2): 349-354. doi: 10.1049/cje.2015.04.021
Citation: XIANG Lin, QU Hanbing, TAO Haijun, WANG Jiaqiang. Variational Bayesian Approximation for Affine Point Set Matching[J]. Chinese Journal of Electronics, 2015, 24(2): 349-354. doi: 10.1049/cje.2015.04.021

Variational Bayesian Approximation for Affine Point Set Matching

doi: 10.1049/cje.2015.04.021
Funds:  This work is supported by International Cooperation Project of Science Technology Department of Zhejiang Province (No.2012C24030), Innovation Group Plan of Beijing Academy of Science and Technology (No.2015-20N), Tianjin Science and Technology Projects(No.14RCGFGX00846).
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  • Corresponding author: QU Hanbing received the M.S. and Ph.D. degrees from Harbin Institute of Technology and Institute of Automation, Chinese Academy of Sciences in 2003 and 2007, respectively. Currently, He is an associate professor in Beijing Institute of New Technology Applications and is the director of Key Laboratory of Pattern Recognition, Beijing Academy of Science and Technology. His research interests include machine learning, pattern recognition and computer vision. (Email:quhanbing@gmail.com)
  • Publish Date: 2015-04-10
  • In this paper, we propose a variational approach for the affine point set matching problems under the Bayesian probabilistic framework. A directed acyclic graph is provided for the representation of the joint probability over affine transformation, random variables and the point sets. Based on the directed graph, a variational iterative algorithm is derived to approximate the posteriors of the random variables and the anisotropic Gaussian mixtures are used for the estimation of the spurious outliers instead of the frequently-used uniform distribution. Experimental results demonstrate that our method achieves good performance in terms of both robustness and accuracy and is comparable to other state-of-the-art point registration algorithms especially in the case of complicated outliers.
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