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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参考文献(48)

[1]	Caetano T S, Caelli T, Schuurmans D, Barone D. Graphical models and point pattern matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(10):1646-1663
[2]	[2] Cootes T F, Taylor C J, Cooper D H, Graham J. Active shape modelstheir training and applications. Computer Vision and Image Understanding, 1995, 61(1):38-59
[3]	[3] Czogiel I, Dryden I, Brignell C. Bayesian matching of unlabeled marked point sets using random fields with application to molecular alignment. Annals of Applied Statistics, 2011, 5(4):2603-2629
[4]	[4] Schmidler S C. Fast Bayesian shape matching using geometric algorithms. Bayesian Statistics, 2007, 8:471-490
[5]	[5] Pilet J, Lepetit V, Fua P. Real-time non-rigid surface detection. In:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA:IEEE, 2005. 822-828
[6]	[6] Simpson I J A, Schnabel J A, Groves A R, Andersson J L R, Woolrich M W. Probabilistic inference of regularisation in non-rigid registration. NeuroImage, 2012, 59(3):2438-2451
[7]	[7] Gu L, Kanade T. A generative shape regularization model for robust face alignment. In:Proceedings of the 10th European Conference on Computer Vision. Marseille, France:Springer, 2008. 413-426
[8]	[8] Wamelen P B V, Li Z, Iyengar S. A fast expected time algorithm for the 2-D point pattern matching problem. Pattern Recognition, 2004, 37(8):1699-1711
[9]	Zhou Zhi-Yong, Li Li-Hua, Zheng Jian, Kuai Duo-Jie, Hu Su, Zhang Tao. Point sets non-rigid registration using student's-t mixture model with spatial constraints. Acta Automatica Sinica, 2014, 40(4):683-696(周志勇, 李莉华, 郑健, 蒯多杰, 胡粟, 张涛. 含局部空间约束的t分布混合模型的点集配准. 自动化学报, 2014, 40(4):683-696)
[10]	Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256
[11]	Lu C P, Mjolsness E. Two-dimensional object localization by coarse-to-fine correlation matching. Advances in Neural Information Processing Systems, 1994, 6:985-992
[12]	Zhang Z. Iterative point matching for registration of freeform curves and surfaces. International Journal of Computer Vision, 1994, 13(2):119-152
[13]	Lowe D G. Object recognition from local scale-invariant features. In:Proceedings of the 7th International Conference on Computer Vision. Kerkyra, Greece:IEEE, 1999, 2:1150 -1157
[14]	Bay H, Tuytelaars T, van Gool L. SURF:speeded up robust features. In:Proceedings of the 9th European Conference on Computer Vision. Graz, Austria:Springer, 2006. 404-417
[15]	Pachauri D, Kondor R, Singh V. Solving the multiway matching problem by permutation synchronization. Advances in Neural Information Processing Systems, 2013, 26:1860-1868
[16]	Pizarro D, Bartoli A. Feature-based deformable surface detection with self-occlusion reasoning. International Journal of Computer Vision, 2012, 97(1):54-70
[17]	Zhao J, Ma J Y, Tian J W, Ma J, Zhang D Z. A robust method for vector field learning with application to mismatch removing. In:Proceedings of the 2011 Computer Vision and Pattern Recognition. Colorado Springs, USA:IEEE, 2011. 2977-2984
[18]	Mardia K V, Nyirongo V B, Fallaize C J, Barber S, Jackson R M. Hierarchical Bayesian modelling of pharmacophores in Bioinformatics. Biometrics, 2011, 67(2):617-619
[19]	Mardia K V, Fallaize C J, Barber S, Jackson R M, Theobald D L. Bayesian alignment of similarity shapes. Annals of Applied Statistics, 2013, 7(2):989-1009
[20]	Liu Y. Automatic 3D free form shape matching using the graduated assignment algorithm. Pattern Recognition, 2005, 38(10):1615-1631
[21]	Tsin T, Kanade T. A correlation-based approach to robust point set registration. In:Proceedings of the 8th European Conference on Computer Vision. Prague, Czech:Springer, 2004. 558-569
[22]	Chui H, Rangarajan A. A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding, 2003, 89(2-3):114-141
[23]	Jian B, Vemuri C C. Robust point set registration using Gaussian mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8):1633-1645
[24]	Ma J Y, Zhao J, Tian J W, Tu Z W, Yuille A. Robust estimation of nonrigid transformation for point set registration. In:Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, Ohio, USA:IEEE, 2013. 2147-2154
[25]	Myronenko A, Song X. Point set registration:coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12):2262-2275
[26]	Horaud R P, Forbes F, Yguel M, Dewaele G D, Zhang J. Rigid and articulated point registration with expectation conditional maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3):587-602
[27]	Almhdie A, Lger C, Deriche M, Lde R. 3D registration using a new implementation of the ICP algorithm based on a comprehensive lookup matrix:application to medical imaging. Pattern Recognition Letters, 2007, 28(12):1523-1533
[28]	Dorai C, Wang G, Jain A, Mercer C. From images to models:automatic 3D object model construction from multiple views. In:Proceedings of the 13th International Conference on Pattern Recognition. Vienna, Austria:IEEE, 1996. 770-774
[29]	Hahnel D, Thrun S, Burgard W. An extension of the ICP algorithm for modelling nonrigid objects with mobile robots. In:Proceedings of the 18th International Conference on Artificial Intelligence. Acapulco, Mexico:Morgan Kaufmann, 2003. 915-920
[30]	Liu Y. Automatic registration of overlapping 3D point clouds using closest points. Image and Vision Computing, 2006, 24(7):762-781
[31]	Chui H, Rangarajan A. A feature registration framework using mixture models. In:Proceedings of the 2000 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. Hilton Head Island, USA:IEEE, 2000. 190-197
[32]	Hou S, Galata A. Robust estimation of Gaussian mixtures from noisy input data. In:Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA:IEEE, 2008. 1-8
[33]	Govindu V M, Werman M. On using priors in affine matching. Image and Vision Computing, 2004, 22(14):1157-1164
[34]	Hastie T J, Tibshirani R J. Generalized Additive Models. London, UK:Chapman and Hall, 1990.
[35]	D'Souza A, Vijayakumar S, Schaal S. The Bayesian backfitting relevance vector machine. In:Proceedings of the 21st International Conference on Machine Learning. Banff, Alberta, Canada:ACM, 2004. 31
[36]	Hu B G, Qu H B, Wang Y, Yang S H. A generalized-constraint neural network model:associating partially known relationships for nonlinear regression. Information Sciences, 2009, 179(12):1929-1943
[37]	Qu H B, Hu B G. Variational Bayes inference for generalized associative functional networks. In:Proceedings of the 2007 International Joint Conference on Neural Networks. Orlando, FL, USA:IEEE, 2007. 184-189
[38]	Roberts S J, Penny W D. Variational Bayes for generalized autoregressive models. IEEE Transactions on Signal Processing, 2002, 50(9):2245-2257
[39]	Bishop C M. Pattern Recognition and Machine Learning. Spring Street, NY:Springer, 2006.
[40]	Beal M J. Variational Algorithms for Approximate Bayesian Inference [Ph.D. dissertation], University of Cambridge, UK, 2003.
[41]	MacKay D J C. Bayesian non-linear modeling for the prediction competition. Maximum Entropy and Bayesian Methods, 1996, 62:221-234
[42]	Lawrence N D, Bishop C M. Variational Bayesian Independent Component Analysis. Technical Report, Computer Laboratory, University of Cambridge, UK, 2000.
[43]	Beal M, Ghahramani Z. The variational Bayesian EM algorithm for incomplete data:with application to scoring graphical model structures. Bayesian Statistics, 2003, 7:453 -464
[44]	Choudrey R A. Variational Methods for Bayesian Independent Component Analysis [Ph.D. dissertation], University of Oxford, UK, 2002.
[45]	Hensman J, Rattray M, Lawrence N. Fast variational inference in the conjugate exponential family. Advances in Neural Information Processing Systems, 2012, 25:2897-2905
[46]	Valpola H, Honkela A. Hyperparameter Adaptation in Variational Bayes for the Gamma Distribution. Technical Report, Laboratory of Computer and Information Science, Helsinki University of Technology, FI, 2006.
[47]	Kaji D, Watanabe S. Two design methods of hyperparameters in variational Bayes learning for Bernoulli mixtures. Neuralcomputing, 2011, 74(11):2002-2007
[48]	Bergstra J B, Bengio Y. Random search for hyperparameter optimization. Journal of Machine Learning Research, 2012, 13(1):281-305