A mixture model for robust registration in Kinect sensor

Li Peng; Huabing Zhou; Shengguo Zhu

doi:10.1117/12.2282552

8 March 2018 A mixture model for robust registration in Kinect sensor

Li Peng; Huabing Zhou; Shengguo Zhu

Author Affiliations +

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060906 (2018) https://doi.org/10.1117/12.2282552
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

Citation Download Citation

Li Peng, Li Peng, Huabing Zhou, Huabing Zhou, Shengguo Zhu, Shengguo Zhu, } "A mixture model for robust registration in Kinect sensor", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060906 (8 March 2018); doi: 10.1117/12.2282552; https://doi.org/10.1117/12.2282552

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