基于概率图模型的点集匹配方法研究

曲寒冰; 王加强; 李彬; 王松涛

doi:10.16383/j.aas.2015.c140376

基于概率图模型的点集匹配方法研究

doi: 10.16383/j.aas.2015.c140376

曲寒冰^1,2,3,
王加强^1,2,
李彬^1,2,
王松涛^1,2

1.
北京市科学技术研究院模式识别重点实验室北京 100094;
2.
北京市新技术应用研究所北京 100094;
3.
河北工业大学计算机科学与软件学院天津 300401

基金项目:

北京市科学技术研究院创新团队计划(2015-20N),北京市科学技术研究院青年骨干计划(2014-30),天津市科技计划(14RCGFGX00846)资助

详细信息

作者简介:
王加强北京市科学技术研究院模式识别重点实验室助理研究员,河北工业大学微电子学与固体电子学专业博士研究生.2005年和2008年分别获得山东理工大学学士和工学硕士.主要研究方向为图像处理,计算机视觉,模式识别和生物识别.E-mail:wangjq.bj@gmail.com

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- 被引次数: 0
出版历程
- 收稿日期: 2014-06-03
- 修回日期: 2014-09-27
- 刊出日期: 2015-04-20

Probabilistic Graphical Model for Robust Point Set Matching

1.
Key Laboratory of Pattern Recognition, Beijing Academy of Science and Technology, Beijing 100094;
2.
Beijing Institute of New Technology Applications, Beijing 100094;
3.
School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401

Funds:

Supported by Innovation Group Plan of Beijing Academy of Science and Technology(2015-20N), Youth Core Plan of Beijing Academy of Science and Technology(2014-30), and Tianjin Science and Technology Projects(14RCGFGX00846)

摘要

摘要: 在概率图模型框架下提出了一种将回归分析和聚类分析相结合的贝叶斯点集匹配方法,其中,回归分析用来估计两个点集之间的映射函数,而聚类分析用来建立两个点集中点与点之间的对应关系.本文将点集匹配问题表示为一种多层的概率有向图,并提出了一种由粗到精的变分逼近算法来估计点集匹配的不确定性;此外,还利用高斯混合模型估计映射函数回归中的异方差噪声和场景点密度估计中离群点的分布;同时,引入转移变量建立起模型点集与场景点集之间的关系,并与离群点混合模型共同对场景点的分布进行估计.实验结果表明,该方法与其他点集匹配算法相比,在鲁棒性和匹配精度方面均达到了较好的效果.
- 点集匹配 /
- 图模型 /
- 变分逼近 /
- 高斯混合模型 /
- 鲁棒估计
Abstract: In this work, we propose a combinative strategy of regression and clustering for point set matching problems under Bayesian framework, with regression for estimation of transformation and clustering for establishment of correspondence. The structure of matching problem is represented by a hierarchical graph model and the matching uncertainty is approximated by a coarse-to-fine variational inference algorithm. Furthermore, Gaussian mixture models are proposed for the density estimation of heteroscedastic regression noise and spurious outliers in the {scene}, and the isotropic or anisotropic covariance is imposed on each individual mixture component in terms of the transformed {model} points. Experimental results show that the proposed approach achieves comparable performance to the state-of-the-art matching algorithms in both robustness and accuracy.
- Point set matching /
- graphical model /
- variational approximation /
- Gaussian mixture model /
- robust estimation

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