基于变分贝叶斯逼近的前向仿射变换点集匹配方法研究

曲寒冰; 陈曦; 王松涛; 于明

doi:10.16383/j.aas.2015.e130204

基于变分贝叶斯逼近的前向仿射变换点集匹配方法研究

doi: 10.16383/j.aas.2015.e130204

曲寒冰^1,3,,
陈曦^2,3,
王松涛¹,
于明^2,3

1.
北京市科学技术研究院，模式识别重点实验室北京100094;
2.
河北工业大学，电子信息工程学院天津3000401 ;
3.
河北工业大学，计算机科学与软件学院天津 300401

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出版历程
- 收稿日期: 2013-08-30
- 修回日期: 2015-02-09
- 刊出日期: 2015-08-20

Forward Affine Point Set Matching Under Variational Bayesian Framework

QU Han-Bing^{1,3
,},
CHEN Xi^2,3,
WANG Song-Tao¹,
YU Ming^2,3

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

Funds:

Supported by Innovation Group Plan of Beijing Academy of Science and Technology (IG201506N), Youth Core Plan of Beijing Academy of Science and Technology (2014-30), Tianjin Science and Technology Projects (14RCGFGX00846)

More Information

Corresponding author: QU Han-Bing Received the M.S.and Ph.D.degrees from Harbin Institute of Technology (HIT) and Institute of Automation,Chinese Academy of Sciences (CASIA) in 2003 and 2007,respectively.Currently,He is an associate professor in Beijing Institute of New Technology Applications and is the director of Key Laboratory of Pattern Recognition,Beijing Academy of Science and Technology (BJAST).He is also a committee member of Intelligent Automation Committee of Chinese Association of Automation (IACAA).His research interest covers biometrics,machine learning,pattern recognition and computer vision.Corresponding author of this paper.E-mail:quhanbing@gmail.com

摘要

摘要: 本文建立了两个点集线性匹配过程的贝叶斯模型框架，并利用变分贝叶斯逼近方法对模型点集到场景点集的仿射参数进行估计。该模型利用一个有向图对映射参数、隐藏变量、模型与场景点集的关系进行了描述，并基于有向图给出了各个参数和变量后验概率的迭代估计算法。而且该模型还利用了一个带有各向异性协方差矩阵的高斯模型对场景点集的离群点进行了估计和推理。实验结果表明该模型在鲁棒性和匹配精度方面均获得了良好的效果。
- 点集匹配 /
- 变分贝叶斯 /
- 仿射变换 /
- 各向异性高斯模型
Abstract: In this work, the affine point set matching is formulated under a variational Bayesian framework and the model points are projected forward into the scene space by a linear transformation. A directed acyclic graph is presented to represent the relationship between the parameters, latent variables, model and scene point sets and an iterative approximate algorithm is proposed for the estimation of the posterior distributions over parameters. Furthermore, the anisotropic covariance is assumed on the transition variable and one Gaussian component is provided for the inference of outlier points. Experimental results demonstrate that the proposed algorithm achieves good performance in terms of both robustness and accuracy.
- Point set matching /
- variational Bayesian /
- affine transformation /
- graphical model

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