基于变分贝叶斯的自适应非刚性点集匹配  

Adaptive Non-Rigid Point Set Registration Based on Variational Bayesian

作　　者：杨丽娟[1] 田铮[1,2] 温金环[1] 延伟东[1] Yang Lijuan;Tian Zheng;Wen Jinhuan;Yan Weidong(Department of Applied Mathematics,Northwestern Polytechnical University,Xi′an 710129,China;The State Key Laboratory of Remote Sensing Science,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[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

出　　处：《西北工业大学学报》2018年第5期942-948,共7页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金青年科学基金(61201323;61301196);国家自然科学基金面上项目(60972150);遥感科学国家重点实验室开放基金(OFSLRSS201206);陕西省自然科学基础研究计划(2017JM6026);陕西省教育厅专项科研计划项目(16JK1326)资助

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。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.

关 键 词：非刚性 点集匹配 变分贝叶斯 混合t分布 异常值 稳健 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]