Non-rigid Point Set Registration Based on Neighbor Structure and Gaussian Mixture Models

PENG Lei; LI Guangyao; XIAO Mang; WANG Gang; XIE Li

doi:10.11999/JEIT150501

Volume 38 Issue 1

Jan. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(1): 47-52

PENG Lei, LI Guangyao, XIAO Mang, WANG Gang, XIE Li. Non-rigid Point Set Registration Based on Neighbor Structure and Gaussian Mixture Models[J]. Journal of Electronics & Information Technology, 2016, 38(1): 47-52. doi: 10.11999/JEIT150501

Citation:

PENG Lei, LI Guangyao, XIAO Mang, WANG Gang, XIE Li. Non-rigid Point Set Registration Based on Neighbor Structure and Gaussian Mixture Models[J]. Journal of Electronics & Information Technology, 2016, 38(1): 47-52. doi: 10.11999/JEIT150501

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Non-rigid Point Set Registration Based on Neighbor Structure and Gaussian Mixture Models

doi: 10.11999/JEIT150501

1.
2.
(College of Electronic Information and Engineering, Tongji University, Shanghai 201804, China)

Funds:

Shandong Provincial Natural Science Foundation, China (ZR2015FL005), Taian Science and Technology Development Program, China (2015GX2016)

Received Date: 2015-04-30
Rev Recd Date: 2015-10-08
Publish Date: 2016-01-19

Abstract

Abstract

In the practical application, non-rigid point set registration should be robust for noise, occlusion or outliers. In this paper, Gaussian Mixture Model (GMM) and neighborhood structure information are used for the non-rigid point set registration. Gaussian Mixture Model is used to represent the model set, and the transformation is built by using Gaussian radial basis function. The proportion of each Gaussian component is decided by the neighborhood structure information of points. In E-step of the EM algorithm the correspondence is solved, and in M-step the outlier ratio and the closed-form solution of the transformation are calculated. Until convergence the optimal solution is obtained. As compared to the state-of-the-art algorithms, the experiments with synthetic data and real data of the retina images show that the proposed method can improve the robustness and the accuracy.
- Image registration,
- Non-rigid point set registration,
- Gaussian mixture model,
- Structure descriptor

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References(20)

References

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