Top

International Journal of Machine Learning and Cybernetics

Published in:

30-10-2022 | Original Article

Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework

Authors: Lijuan Yang, Nannan Ji, Changpeng Wang, Tianjun Wu, Fuxiao Li

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In order to establish effective correspondences, a two-phase registration method for three-dimensional point set is proposed under the Bayesian mixture framework. In the first phase, the mixture model consisted of student’s t distribution and von Mises-Fisher (vMF) distribution is designed to perform similarity point set registration for recovering rotation transformation, where both distributions are used to measure positional and directional errors, respectively. The second phase implements nonrigid (affine as a particular case) registration between data point set and transformed model point set obtained in the first phase, which is based on student’s t mixture model (SMM) using positional information only. In each phase, variational inference is used to obtain approximate posteriors of model parameters. The experimental results on various datasets demonstrate that our proposed method can achieve better registration performance in terms of robustness to rotation and outliers.

previous article Incremental approaches for optimal scale selection in dynamic multi-scale set-valued decision tables

next article Correction to: robust two-phase registration method for three-dimensional point set under the bayesian mixture framework

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information.

Order your 30-days-trial for free and without any commitment.

inform now

ATZelektronik

Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik.

Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.

inform now

Available only for authorised users

https://www.dir-lab.com/Downloads.html.

http://graphics.stanford.edu/data/3Dscanrep/.

http://alice.loria.fr/index.php/software/7-data/37-unwrapped-meshes.html.

https://gfx.cs.princeton.edu/proj/sugcon/models/.

Beal MJ (2003) Variational algorithms for approximate Bayesian inference. Dissertation, University of London

Bing J, Vemuri BC (2011) Robust point set registration using Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 33(8):1633–1645. https://doi.org/10.1109/TPAMI.2010.223CrossRef

Bishop CM (2006) Pattern recognition and machine learning. Springer, New YorkMATH

Chui HL, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Comput Vis Image Understand 89(2–3):114–141. https://doi.org/10.1016/S1077-3142(03)00009-2CrossRefMATH

Fan AX, Jiang XY, Ma Y, Mei XG, Ma JY (2021) Smoothness-driven consensus based on compact representation for robust feature matching. IEEE Trans Neural Netw Learn Syst 99:1–1. https://doi.org/10.1109/TNNLS.2021.3118409CrossRef

Goshtasby AA (2005) 2-D and 3-D image registration: for medical, remote sensing, and industrial applications. Wiley-Interscience, Dayton

He QQ, Zhou J, Xu SJ, Yang Y, Liu Y, Liu YH (2020) Adaptive hierarchical probabilistic model using structured variational inference for point set registration. IEEE Trans Fuzzy Syst 98(11):2784–2798. https://doi.org/10.1109/TFUZZ.2020.2974433CrossRef

Hirose O (2021) A Bayesian formulation of coherent point drift. IEEE Trans Pattern Anal Mach Intell 43(7):2269–2286. https://doi.org/10.1109/TPAMI.2020.2971687CrossRef

Ma JY, Zhao J, Tian JW, Bai X, Tu ZW (2013) Regularized vector field learning with sparse approximation for mismatch removal. Pattern Recogn 46(12):3519–3532. https://doi.org/10.1016/j.patcog.2013.05.017CrossRefMATH

10.

Ma JY, Zhao J, Tian JW, Yuille AL, Tu ZW (2014) Robust point matching via vector field consensus. IEEE Trans Image Process 23(4):1706–1721. https://doi.org/10.1109/TIP.2014.2307478MathSciNetCrossRefMATH

11.

Ma JY, Qiu WC, Zhao J, Ma Y, Yuille AL, Tu ZW (2015) Robust L₂E estimation of transformation for non-rigid registration. IEEE Trans Signal Process 63(5):1115–1129. https://doi.org/10.1109/TSP.2014.2388434MathSciNetCrossRefMATH

12.

Ma JY, Zhou HB, Zhao J, Gao Y, Jiang JJ, Tian JW (2015) Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans Geosci Remote Sens 53(12):6469–6481. https://doi.org/10.1109/TGRS.2015.2441954CrossRef

13.

Ma JY, Wu J, Zhao J, Jiang JJ, Zhou HB, Sheng QZ (2018) Nonrigid point set registration with robust transformation learning under manifold regularization. IEEE Trans Neural Netw Learn Syst 30(12):3584–3597. https://doi.org/10.1109/TNNLS.2018.2872528MathSciNetCrossRef

14.

Ma JY, Jiang XG, Jiang JJ, Gao Y (2019) Feature-guided Gaussian mixture model for image matching. Pattern Recogn 92:231–245. https://doi.org/10.1016/j.patcog.2019.04.001CrossRef

15.

Ma JY, Zhao J, Jiang JJ, Zhou HB, Guo XJ (2019) Locality preserving matching. Int J Comput Vis 127(2):512–531. https://doi.org/10.1007/s11263-018-1117-zMathSciNetCrossRefMATH

16.

Ma JY, Jiang XY, Fan AX, Jiang JJ, Yan JC (2021) Image matching from handcrafted to deep features: a survey. Int J Comput Vis 129(1):23–79. https://doi.org/10.1007/s11263-020-01359-2MathSciNetCrossRefMATH

17.

Ma XK, Xu SJ, Zhou J, Yang QL, Yang Y, Yang K, Ong SH (2020) Point set registration with mixture framework and variational inference. Pattern Recogn 104(3):107345. https://doi.org/10.1016/j.patcog.2020.107345CrossRef

18.

Maiseli B, Gu YF, Gao HJ (2017) Recent developments and trends in point set registration methods. J Vis Commun Image R 46:95–106. https://doi.org/10.1016/j.jvcir.2017.03.012CrossRef

19.

Mclachlan GL, Peel D (2000) Finite mixture model. Wiley, Chichester. https://doi.org/10.1002/0471721182CrossRefMATH

20.

Min Z, Wang JL, Meng QH (2020) Robust generalized point cloud registration with orientational data based on expectation maximization. IEEE Trans Autom Sci Eng 17(1):207–221. https://doi.org/10.1109/TASE.2019.2914306CrossRef

21.

Min Z, Meng Max QH (2021) Robust and accurate nonrigid point set registration algorithm to accommodate anisotropic positional localization error based on coherent point drift. IEEE Trans Autom Sci Eng 18(4):1939–1955. https://doi.org/10.1109/TASE.2020.3027073CrossRef

22.

Min Z, Wang JL, Pan J, Meng QH (2021) Generalized 3-D point set registration with hybrid mixture models for computer-assisted orthopedic surgery: from isotropic to anisotropic positional error. IEEE Trans Autom Sci Eng 18(4):1679–1691. https://doi.org/10.1109/tase.2020.3014420CrossRef

23.

Min Z, Zhu DL, Ren HL, Meng QH (2021) Feature-guided nonrigid 3-D point set registration framework for image-guided liver surgery: from isotropic positional noise to anisotropic positional noise. IEEE Trans Autom Sci Eng 18(2):471–483. https://doi.org/10.1109/TASE.2020.3001207CrossRef

24.

Moigne JL, Netanyahu N, Eastman RD (2011) Image registration for remote sensing. Cambridge University Press, CambridgeCrossRefMATH

25.

Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, Cambridge, Massachusetts, LondonMATH

26.

Myronenko A, Song XB (2010) Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell 32(12):2262–2275. https://doi.org/10.1109/TPAMI.2010.46CrossRef

27.

Oliveira FPM, Tavares JMRS (2014) Medical image registration: a review. Comput Method Biomech 17(2):73–93. https://doi.org/10.1080/10255842.2012.670855CrossRef

28.

Peel D, Mclachlan GJ (2000) Robust mixture modelling using the t distribution. Statis Comput 10:339–348. https://doi.org/10.1023/A:1008981510081CrossRef

29.

Qu HB, Wang JQ, Li B, Yu M (2016) Probabilistic model for robust affine and non-rigid point set matching. IEEE Trans Pattern Anal Mach Intell 39(2):371–384. https://doi.org/10.1109/TPAMI.2016.2545659CrossRef

30.

Ravikumar N, Gooya A, Frangi AF, Taylor ZA (2017) Generalised coherent point drift for group-wise registration of multi-dimensional point sets. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 10433, pp 309–316. https://doi.org/10.1007/978-3-319-66182-7_36

31.

Subrahmanya N, Shin YC (2013) A variational Bayesian framework for group feature selection. Int J Mach Learn Cyber 4(6):609–619. https://doi.org/10.1007/s13042-012-0121-9CrossRef

32.

Tam GKL, Cheng ZQ, Lai YK, Langbein FC, Liu YH, Marshall D, Martin RR, Sun XF, Rosin PL (2013) Registration of 3D point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans Vis Comput Graph 19(7):1199–1217. https://doi.org/10.1109/TVCG.2012.310CrossRef

33.

Wu C, Wang YY, Karimi HR (2014) A robust aerial image registration method using Gaussian mixture models. Neurocomputing 144:546–552. https://doi.org/10.1016/j.neucom.2014.04.012CrossRef

34.

Yang Y, Ong S, Foong K (2015) A robust global and local mixture distance based on nonrigid point set registration. Pattern Recognit 48(1):156–173. https://doi.org/10.1016/j.patcog.2014.06.017CrossRef

35.

Yang LJ, Tian Z, Wen JH, Yan WD (2018) Adaptive non-rigid point set registration based on variational Bayesian. J Northwest Polytech Univ 36(5):942–948. https://doi.org/10.1051/jnwpu/20183650942CrossRef

36.

Zhang PP, Qiao Y, Wang SZ, Yang J, Zhu YM (2017) A robust coherent point drift approach based on rotation invariant shape context. Neurocomputing 219:455–473. https://doi.org/10.1016/j.neucom.2016.09.058CrossRef

37.

Zhou ZY, Zheng J, Dai YK, Zhou Z, Chen S (2014) Robust non-rigid point set registration using student’s-t mixture model. PLoS ONE 9(3):e91381. https://doi.org/10.1371/journal.pone.0091381CrossRef

Title: Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework
Authors: Lijuan Yang
Nannan Ji
Changpeng Wang
Tianjun Wu
Fuxiao Li
Publication date: 30-10-2022
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Machine Learning and Cybernetics / Issue 6/2023
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-022-01673-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

ATZelectronics worldwide

ATZelektronik

Other articles of this Issue 6/2023

Enhanced feature Fusion structure of YOLO v5 for detecting small defects on metal surfaces

Software defect prediction ensemble learning algorithm based on adaptive variable sparrow search algorithm

Context-sensitive graph representation learning

Incremental approaches for optimal scale selection in dynamic multi-scale set-valued decision tables

Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction

Conv-PVT: a fusion architecture of convolution and pyramid vision transformer