Volume 36, Number 5, October 2018
|Page(s)||942 - 948|
|Published online||17 December 2018|
Adaptive Non-Rigid Point Set Registration Based on Variational Bayesian
Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an
2 The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing 100101, China
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.
Key words: non-rigid / point set registration / variational Bayesian / student's t mixture model / outliers / robust
关键字 : 非刚性 / 点集匹配 / 变分贝叶斯 / 混合t分布 / 异常值 / 稳健
© 2018 Journal of Northwestern Polytechnical University. All rights reserved.
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