Variational Bayesian Approximation for Affine Point Set Matching

XIANG Lin; QU Hanbing; TAO Haijun; WANG Jiaqiang

doi:10.1049/cje.2015.04.021

Volume 24 Issue 2

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XIANG Lin, QU Hanbing, TAO Haijun, et al., “Variational Bayesian Approximation for Affine Point Set Matching,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 349-354, 2015, doi: 10.1049/cje.2015.04.021

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XIANG Lin, QU Hanbing, TAO Haijun, et al., “Variational Bayesian Approximation for Affine Point Set Matching,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 349-354, 2015, doi: 10.1049/cje.2015.04.021

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Variational Bayesian Approximation for Affine Point Set Matching

doi: 10.1049/cje.2015.04.021

1.
College of Information Engineering, China Jiliang University, Hangzhou 310018, China;
2.
Key Laboratory of Pattern Recognition, Beijing Academy of Science and Technology, Beijing 100094, China;
3.
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China

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).

More Information

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

Abstract

Abstract

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.
- Point set matching,
- Variational inference,
- Affine transformation,
- Gaussian mixture model

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References(14)

References

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