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A mixture model for robust registration in Kinect sensor
Author(s): Li Peng; Huabing Zhou; Shengguo Zhu
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Paper Abstract

The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low registration precision between color image and depth image. In this paper, we present a robust method to improve the registration precision by a mixture model that can handle multiply images with the nonparametric model. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS).The estimation is performed by the EM algorithm which by also estimating the variance of the prior model is able to obtain good estimates. We illustrate the proposed method on the public available dataset. The experimental results show that our approach outperforms the baseline methods.

Paper Details

Date Published: 8 March 2018
PDF: 5 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060906 (8 March 2018); doi: 10.1117/12.2282552
Show Author Affiliations
Li Peng, HuBei Radio & TV Univ. (China)
Huazhong Univ. of Science and Technology (China)
Huabing Zhou, Hubei Provincial Key Lab. of Intelligent Robot (China)
Shengguo Zhu, Huazhong Institute of Electro-Optics (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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