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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 433–444Cite as

Robust Point Matching Using Mixture of Asymmetric Gaussians for Nonrigid Transformation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

Abstract

In this paper, we present a novel robust method for point matching under noise, deformation, occlusion and outliers. We introduce a new probability model to represent point sets, namely asymmetric Gaussian (AG), which can capture spatially asymmetric distributions. Firstly, we use a mixture of AGs to represent the point set. Secondly, we use \(L_2\)-minimizing estimate (\(L_2E\)), a robust estimator to estimate densities between two point sets, to estimate the transformation function in reproducing kernel Hilbert space (RKHS) with regularization theory. Thirdly, we use low-rank kernel matrix approximation to reduce the computational complexity. Experimental results show that our method outperforms the comparative state-of-the-art methods on most scenarios, and it is quite robust to noise, deformation, occlusion and outliers.

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Notes

  1. 1.

    http://www.robots.ox.ac.uk/~vgg/data/.

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Acknowledgement

This work was supported by National Natural Science Foundation of China (NSFC, No. 61103070).

Author information

Authors and Affiliations

  1. CAD Research Center, Tongji University, Shanghai, 201804, China

    Gang Wang, Zhicheng Wang, Weidong Zhao & Qiangqiang Zhou

Authors
  1. Gang Wang
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  2. Zhicheng Wang
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  3. Weidong Zhao
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  4. Qiangqiang Zhou
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Corresponding author

Correspondence to Zhicheng Wang .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Wang, G., Wang, Z., Zhao, W., Zhou, Q. (2015). Robust Point Matching Using Mixture of Asymmetric Gaussians for Nonrigid Transformation. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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