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Information granule-based multi-view point sets registration using fuzzy c-means clustering

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Abstract

This paper addresses the registration problem for multi-view point sets. Motivated by the formation of information granule and casting registration as a clustering task, an information granule-based multi-view point sets registration using fuzzy c-means clustering is proposed. Information granules are formed following the principle of justifiable granularity, and the data points covered by information granules can be obtained to represent the structural crux of the point set. The preprocessing step using information granule can achieve point set simplification and enhance the robustness of subsequent registration. Then, the aligned point sets involved in multi-view registration are clustered, and fuzzy clustering is used to solve the clustering problem and multi-view registration problem simultaneously. Membership function is introduced into the clustering-based registration, which improves the registration performance in comparison with other clustering-based methods with hard partition. Finally, the clustering and transformation estimation are alternately and iteratively applied to all point sets until the final clustering and registration results are obtained. Experiments using publicly benchmark datasets demonstrate that the proposed approach achieves better performance than the comparison approaches in terms of the accuracy and robustness for multi-view registration.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 80:31401–31433

    Article  Google Scholar 

  2. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inform Sci 577:852–870

    Article  MathSciNet  Google Scholar 

  3. Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233–247

    Article  Google Scholar 

  4. Arrigoni F, Rossi B, Fusiello A (2016) Global registration of 3d point sets via lrs decomposition. In: European conference on computer vision, pp 489–504

  5. Bergevin R, Soucy M, Gagnon H, Laurendeau D (1996) Towards a general multi-view registration technique. IEEE Trans Pattern Anal Mach Intell 18(5):540–547

    Article  Google Scholar 

  6. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  7. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Adv Applic Pattern Recogn 22(1171):203–239

    MATH  Google Scholar 

  8. Blais G, Levine MD (1995) Registering multiview range data to create 3d computer objects. IEEE Trans Pattern Anal Mach Intell 17(8):820–824

    Article  Google Scholar 

  9. Castellani U, Fusiello A, Murino V (2002) Registration of multiple acoustic range views for underwater scene reconstruction. Comput Vis Image Underst 87 (1–3):78–89

    Article  MATH  Google Scholar 

  10. Cao H, Wang H, Zhang N, Yang Y, Zhou Z (2022) Robust probability model based on variational bayes for point set registration. Knowl-Based Syst 241(6):108182

    Article  Google Scholar 

  11. Chen H, Sun D, Liu W, Huang X, Liu PX (2020) An automatic registration approach to laser point sets based on multidiscriminant parameter extraction. IEEE Trans Instrum Meas 69(12):9449–9464

    Article  Google Scholar 

  12. Chen H, Liang M, Liu W, Wang W, Liu PX (2022) An approach to boundary detection for 3D point clouds based on DBSCAN clustering. Pattern Recogn 124:108431

    Article  Google Scholar 

  13. Chetverikov D, Svirko D, Stepanov D, Krsek P (2002) The trimmed iterative closest point algorithm. In: Conference on pattern recognition, pp 545–548

  14. Evangelidis GD, Horaud R (2018) Joint alignment of multiple point sets with batch and incremental expectation-maximization. IEEE Trans Pattern Anal Mach Intell 40(6):1397–1410

    Article  Google Scholar 

  15. Ferrari V, Cattari N, Fontana U, Cutolo F (2022) Parallax free registration for augmented reality optical see-through displays in the peripersonal Space. IEEE Trans Vis Comput Graph 28(3):1608–1618

    Article  Google Scholar 

  16. Fu K, Liu Y, Wang M (2021) Global registration of 3d cerebral vessels to its 2d projections by a new branch-and-bound algorithm. IEEE Trans Med Robot Bionics 3(1):115–124

    Article  Google Scholar 

  17. Guo R, Chen J, Wang L (2021) Hierarchical k-means clustering for registration of multi-view point sets. Comput Electr Eng 94:107321

    Article  Google Scholar 

  18. Govindu VM, Pooja A (2014) On averaging multiview relations for 3d scan registration. IEEE Trans Image Process 23(3):1289–1302

    Article  MathSciNet  MATH  Google Scholar 

  19. Govindu VM (2001) Combining two-view constraints for motion estimation. In: IEEE Conference on computer vision and pattern recognition, pp 1–13

  20. Hartley R, Trumpf J, Dai Y, Li H (2013) Rotation averaging. Int J Comput Vis 103(3):267–305

    Article  MathSciNet  MATH  Google Scholar 

  21. Hirose O (2021) A bayesian formulation of coherent point drift. IEEE Trans Pattern Anal Mach Intell 43(7):2269–2286

    Article  Google Scholar 

  22. Horaud R, Forbes F, Yguel M, Dewaele G, Zhang J (2011) Rigid and articulated point registration with expectation conditional maximization. IEEE Trans Pattern Anal Mach Intell 33(3):587–602

    Article  Google Scholar 

  23. Huber DF, Hebert M (2003) Fully automatic registration of multiple 3d data sets. Image Vis Comput 21(7):637–650

    Article  Google Scholar 

  24. Izadi S, Kim D, Hilliges O, Molyneaux D, Fitzgibbon AW (2011) Kinect fusion: real-time 3d reconstruction and interaction using a moving depth camera. In: Proceedings of annual ACM symposium on user interface software and technology, pp 559–568

  25. Li Y, Snavely N, Huttenlocher DP (2010) Location recognition using prioritized feature matching. In: European conference on computer vision, pp 791–804

  26. Li L, Yang M, Wang C, Wang B (2020) Robust point set registration using signature quadratic form distance. IEEE Trans Cybern 50(5):2097–2109

    Article  Google Scholar 

  27. Liao QF, Sun D, Andreasson H (2021) Point set registration for 3d range scans using fuzzy cluster-based metric and efficient global optimization. IEEE Trans Pattern Anal Mach Intell 43(9):3229–3246

    Article  Google Scholar 

  28. Levoy M, Gerth J, Curless B, Pull K (2005) The stanford 3d scanning repository. http://www-graphics.stanford.edu/data/3dscanrep

  29. Ma X, Xu S, Zhou J, Yang Q, Ong SH (2020) Point set registration with mixture framework and variational inference. Pattern Recogn 104:107345

    Article  Google Scholar 

  30. Masuda T, Yokoya N (1995) A robust method for registration and segmentation of multiple range images. Comput Vis Image Underst 61(3):295–307

    Article  Google Scholar 

  31. Min Z, Wang J, Meng QH (2020) Joint rigid registration of multiple generalized point sets with hybrid mixture models. IEEE Trans Autom Sci Eng 17 (1):334–347

    Article  Google Scholar 

  32. Myronenko A, Song X (2010) Point-set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell 32(12):2262–2275

    Article  Google Scholar 

  33. Nchtera A, Elseberga J, Schneiderb P, Paulusb D (2010) Study of parameterizations for the rigid body transformations of the scan registration problem. Comput Vis Image Underst 114(8):963–980

    Article  Google Scholar 

  34. Pan J, Mai X, Wang C, Min Z, Meng QH (2021) A searching space constrained partial to full registration approach with applications in airport trolley deployment robot. IEEE Sensors J 21(10):11946– 11960

    Article  Google Scholar 

  35. Pedrycz W, Homenda W (2013) Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 13(10):4209–4218

    Article  Google Scholar 

  36. Pedrycz W, Succi G, Sillitti A, Iljazi J (2015) Data description: a general framework of information granules. Knowl-Based Syst 80:98–108

    Article  Google Scholar 

  37. Peng W, Wang Y, Zhang H, Zhu Q, Miao Z, Feng M (2022) Stochastic joint alignment of multiple point clouds for profiled blades 3-d reconstruction. IEEE Trans Ind Electron 69(2):1682–1693

    Article  Google Scholar 

  38. Rusinkiewicz S (2019) A symmetric objective function for ICP. ACM Trans Graph 38(4):1–7

    Article  Google Scholar 

  39. Segal AV, Haehnel D, Thrun S (2009) Generalized-icp. In: Robotics: science and systems, pp 161–168

  40. Sun L, Zhang Z, Zhong R, et al. (2022) A weakly supervised graph deep learning framework for point cloud registration. IEEE Trans Geosci Remote Sens 60:1–12

    Article  Google Scholar 

  41. Tian Z, Liu J, Li Z, Zhu J, Du S (2020) Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration. Neurocomputing 413:230–239

    Article  Google Scholar 

  42. Wang Y, Solomon J (2019) Deep closest point: learning representations for point cloud registration. In: IEEE/CVF International conference on computer vision, pp 3522–3531

  43. Yong PA, Ms C, Lz B, Xs A, Wei SA (2021) PR-FCM: a polynomial regression-based fuzzy c-means algorithm for attribute-associated data. Inform Sci 585:209–231

    Google Scholar 

  44. Zhang J, Yao Y, Deng B (2022) Fast and robust iterative closet point. IEEE Trans Pattern Anal Mach Intell 44(7):3450–3466

    Google Scholar 

  45. Zhu J, Guo R, Li Z, Zhang J, Pang S (2020) Registration of multi-view point sets under the perspective of expectation-maximization. IEEE Trans Image Process 29:9176–9189

    Article  MathSciNet  MATH  Google Scholar 

  46. Zhu J, Jiang Z, Evangelidis GD, Zhang C, Pang S, Li Z (2019) Efficient registration of multi-view point sets by k-means clustering. Inform Sci 488:205–218

    Article  MathSciNet  MATH  Google Scholar 

  47. Zhu J, Li Z, Du S, Ma L, Zhang T (2014) Surface reconstruction via efficient and accurate registration of multiview range scans. Opt Eng 53 (10):102–104

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 62266046 and the Natural Science Foundation of Jilin Province, China, under Grant YDZJ202201ZYTS603.

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Authors and Affiliations

  1. College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 13022, Jilin, China

    Weina Wang & Kai Lin

  2. Laser Business Division, Suzhou Maxwell Technology Co., Ltd, Suzhou, 215200, Jiangsu, China

    Kai Lin

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  1. Weina Wang
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Wang, W., Lin, K. Information granule-based multi-view point sets registration using fuzzy c-means clustering. Multimed Tools Appl 82, 17283–17300 (2023). https://doi.org/10.1007/s11042-022-14250-8

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