Adaptive Non-Rigid Point Set Registration Based on Variational Bayesian

基于变分贝叶斯的自适应非刚性点集匹配

¹ Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an 710129, China
² The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing 100101, China

Received: 12 September 2017

Abstract

For the existence of outliers in non-rigid point set registration, a method based on Bayesian student's t mixture model(SMM) is proposed. Under the framework of variational Bayesian, the point set registration problem is converted to maximize the variational lower bound of log-likelihood, where the transformation parameters are found through variational inference. By prior model, the constraint over spatial regularization is incorporated into the Bayesian SMM, which can adaptively be determined for different data sets. Compared with Gaussian distribution, the student's t distribution is more robust to outliers. The experimental comparative analysis of simulated points and real images verify the effectiveness of the proposed method on the non-rigid point set registration with outliers.

摘要

针对存在异常值的非刚性点集匹配问题，提出了一种基于贝叶斯混合t分布模型的匹配方法。在变分贝叶斯框架下，点集匹配问题转化为最大化对数似然的变分下界，利用变分推断确定变换参数。利用先验模型，将空间正则化约束并入贝叶斯混合t分布模型中，根据不同的点集可自适应地确定正则化参数。与高斯分布相比，t分布对异常值更加稳健。最后，在模拟点集和真实图像上的实验对比分析，验证了该方法在处理存在异常值的非刚性点集匹配问题时的有效性。