基于贝叶斯学生t分布混合的稳健点集匹配

杨丽娟; 田铮; 温金环; 延伟东

doi:doi:10.3788/lop55.012801

激光与光电子学进展, 2018, 55 (1): 012801, 网络出版: 2018-03-22

基于贝叶斯学生t分布混合的稳健点集匹配下载： 677次

Robust Point Set Registration Based on Bayesian Student′s t Mixture Model

论文大纲

杨丽娟 ^1,*田铮 ^1,2温金环 ¹延伟东 ¹

作者单位

¹ 西北工业大学应用数学系, 陕西西安 710129

² 中国科学院遥感科学国家重点实验室, 北京 100101

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摘要

针对点集匹配中异常值的干扰问题, 提出了一种基于贝叶斯学生t分布混合模型(SMM)的稳健仿射点集匹配方法。在贝叶斯框架下, 该算法将点集匹配问题模型化为利用SMM进行概率密度估计的问题。通过引入模型参数的近似变分后验分布, 目标函数转化为最大化完全数据对数似然的变分下界, 利用变分贝叶斯期望最大化(VBEM)算法迭代估计模型参数的变分后验分布。对于学生t分布的自由度参数, 通过最大化完全数据的对数似然进行迭代更新, 并利用斯特林公式近似计算。通过模拟点集和光学遥感图像的配准实验, 验证了该方法的有效性。

Abstract

For the interference problem of outliers on the point set registration, a robust affine point set registration method based on Bayesian student′s t mixture model (SMM) is proposed. Under Bayesian framework, the point set registration problem is formularized as the probability density estimation problem by using the SMM. By introducing the approximate variational posterior distribution, the objective function is converted to maximize the variational lower bound of complete data log-likelihood, and the variational Bayesian expectation maximization (VBEM) method is used to estimate the variational posterior distribution of model parameters iteratively. The free degree of student t distribution is estimated by maximizing the complete data log-likelihood, and it is approximated by using the Stirling formula. Registration experiments on simulated point sets and optical remote sensing images verify the effectiveness and feasibility of the proposed method.

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杨丽娟, 田铮, 温金环, 延伟东. 基于贝叶斯学生t分布混合的稳健点集匹配[J]. 激光与光电子学进展, 2018, 55(1): 012801. Yang Lijuan, Tian Zheng, Wen Jinhuan, Yan Weidong. Robust Point Set Registration Based on Bayesian Student′s t Mixture Model[J]. Laser & Optoelectronics Progress, 2018, 55(1): 012801.

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