基于邻域结构和高斯混合模型的非刚性点集配准算法

doi:10.11999/JEIT150501

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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.

Key words： Image registration Non-rigid point set registration Gaussian mixture model Structure descriptor

Received: 30 April 2015 Published: 18 November 2015

PACS:

TP391

Fund:

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

Corresponding Authors: PENG Lei E-mail: pengleisd@163.com

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	PENG Lei
	LI Guangyao
	XIAO Mang
	WANG Gang
	XIE Li

Cite this article:

PENG Lei,LI Guangyao,XIAO Mang等. Non-rigid Point Set Registration Based on Neighbor Structure and Gaussian Mixture Models[J]. JEIT, 2016, 38(1): 47-52.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150501 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I1/47

[1]	BESL P J and MCKAY N D. Method for registration of 3-D shapes[C]. Robotics-DL tentative. International Society for Optics and Photonics, Boston, 1992: 586-606.
[2]	CHUI H and RANGARAJAN A. A new point matching algorithm for non-rigid registration[J]. Computer Vision and Image Understanding, 2003, 89(2): 114-141.
[3]	BELONGIE S, MALIK J, and PUZICHA J. Shape matching and object recognition using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522.
[4]	MA J, ZHAO J, TIAN J, et al. Robust estimation of nonrigid transformation for point set registration[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, 2013: 2147-2154.
[5]	CHEN J, MA J, YANG C, et al. Non-rigid point set registration via coherent spatial mapping[J]. Signal Processing, 2015, 106: 62-72.
[6]	ZHENG Y and DOERMANN D. Robust point matching for nonrigid shapes by preserving local neighborhood structures [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 643-649.
[7]	GE S, FAN G, and DING M. Non-rigid point set registration with global-local topology preservation[C]. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, 2014: 245-251.
[8]	TANG J, SHAO L, and ZHEN X. Robust point pattern matching based on spectral context[J]. Pattern Recognition, 2014, 47(3): 1469-1484.
[9]	LEE J H and WON C H. Topology preserving relaxation labeling for nonrigid point matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 427-432.
[10]	YAN X, WANG W, ZHAO J, et al. Relaxation labeling for non-rigid point matching under neighbor preserving[J]. Journal of Central South University, 2013, 20: 3077-3084.
[11]	ZHAO H, JIANG B, TANG J, et al. Image matching using a local distribution based outlier detection technique[J]. Neurocomputing, 2015, 148: 611-618.
[12]	秦红星, 徐雷. 基于信息论的 KL-Reg 点云配准算法[J]. 电子与信息学报, 2015, 37(6): 1520-1524. doi: 10.11999/ JEIT141248.
	QIN Hongxing and XU Lei. Information theory based KL-Reg point cloud registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10. 11999/JEIT141248.
[13]	TAO W and SUN K. Asymmetrical gauss mixture models for point sets matching[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 2014: 1598-1605.
[14]	ZHOU Z, ZHENG J, DAI Y, et al. Robust non-rigid point set registration using student’st mixture model[J]. PlosOne, 2014, 9(3): e91381.
[15]	JIAN B and VEMURI B C. Robust point set registration using gaussian mixture models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1633-1645.
[16]	MYRONENKO A and SONG X. Point set registration: coherent point drift[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12): 2262-2275.
[17]	ZHAO J, MA J, TIAN J, et al. A robust method for vector field learning with application to mismatch removing[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, 2011: 2977-2984.
[18]	赵烨, 蒋建国, 洪日昌. 基于空间约束的快速鲁棒特征匹配优化[J]. 电子与信息学报, 2014, 36(11): 2571-2577. doi: 10.3724/ SP.J.1146.2013.01960.
	ZHAO Ye, JIANG Jianguo, and HONG Richang. A speeded up robust feature matching optimization based on apatial constraint[J]. Journal of Electronics & Information Technology, 2014, 36(11): 2571-2577. doi: 10.3724/ SP.J. 1146.2013.01960.