Yasser Derrar,∗ Farah Saidi,∗∗ and Abed Malti∗∗∗


  1. [1] A. Petit, V. Lippiello, G.A. Fontanelli, and B. Siciliano,Tracking elastic deformable objects with an RGB-d sensorfor a pizza chef robot, Robotics and Autonomous Systems,88, 2017, 187–201.
  2. [2] N. Haouchine, J. Dequidt, I. Peterlik, E. Kerrien, M.-O. Berger,and S. Cotin, Image-guided simulation of heterogeneous tissuedeformation for augmented reality during hepatic surgery,Proc. IEEE Int. Symposium on Mixed and Augmented Reality(ISMAR), Adelaide, 2013, 199–208.
  3. [3] A. Rastegarpanah, A. Aflakian, and R. Stolkin, Optimizedhybrid decoupled visual servoing with supervised learning,Proceedings of the Institution of Mechanical Engineers, PartI: Journal of Systems and Control Engineering, 236(2), 2022,338–354.
  4. [4] C. Chi and D. Berenson, Occlusion-robust deformable objecttracking without physics simulation, Proc. IEEE/RSJ Int.Conf. on Intelligent Robots and Systems (IROS), Macau, 2019,6443–6450.
  5. [5] R. Lagneau, Shape control of deformable objects by adaptivevisual servoing, Ph.D. Thesis, INSA Rennes, Rennes, France,
  6. [6] R. Lagneau, A. Krupa, and M. Marchal, Active deformationthrough visual servoing of soft objects, Proc. IEEE Int. Conf.on Robotics and Automation (ICRA), Paris, 2020, 8978–8984.
  7. [7] F. Chaumette, Visual servoing. (Boston, MA: Springer US,2014), 869–874.
  8. [8] O. Tahri and F. Chaumette, Image moments: Genericdescriptors for decoupled image-based visual servo, Proc. IEEEInt. Conf. on Robotics and Automation, New Orleans, LA,2004, 1185–1190.
  9. [9] D.J. Agravante, G. Claudio, F. Spindler, and F. Chaumette,Visual servoing in an optimization framework for the whole-body control of humanoid robots, IEEE Robotics andAutomation Letters, 2(2), 2017, 608–615.
  10. [10] X. Ren, H. Li, and Y. Li, Image-based visual servoing controlof robot manipulators using hybrid algorithm with featureconstraints, IEEE Access, 8, 2020, 223495–223508.
  11. [11] T. Wang, W. Wang, and F. Wei, An overview of control strategyand trajectory planning of visual servoing, in M. Fei, K. Li, Z.Yang, Q. Niu, and X. Li, (eds.), Recent Featured Applicationsof Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020Workshops. (Singapore: Springer, 2020), 358–370.
  12. [12] P. Corke and S. Hutchinson, A new partitioned approachto image-based visual servo control, IEEE Transactions onRobotics and Automation, 17(4), 2001, 507–515.
  13. [13] N.R. Gans, S.A. Hutchinson, and P.I. Corke, Performance testsfor visual servo control systems, with application to partitionedapproaches to visual servo control, The International Journal ofRobotics Research, 22(10–11), 2003, 955–981.
  14. [14] F. Janabi-Sharifi and W.J. Wilson, Automatic selection ofimage features for visual servoing, IEEE Transactions onRobotics and Automation, 13(6), 1997, 890–903.13
  15. [15] F. Chaumette, Image moments: A general and useful set offeatures for visual servoing, IEEE Transactions on Robotics,20(4), 2004, 713–723.
  16. [16] C. Molnar, T. D. Nagy, R.N. Elek, and T. Haidegger, Visualservoing-based camera control for the da Vinci surgical system,Proc. IEEE 18th Int. Symposium on Intelligent Systems andInformatics (SISY), Subotica, 2020, 107–112.
  17. [17] I. S. Mohamed, MPPI-VS: Sampling-based model predictivecontrol strategy for constrained image-based and position-basedvisual servoing, arXiv preprint arXiv: 2104.04925, April 2021.
  18. [18] R. Lagneau, A. Krupa, and M. Marchal, Active deformationthrough visual servoing of soft objects, Proc. IEEE Int. Conf.on Robotics and Automation (ICRA), Paris, 2020, 8978–8984.
  19. [19] Z. Hu, T. Han, P. Sun, J. Pan, and D. Manocha, 3-D deformableobject manipulation using deep neural networks, IEEE Roboticsand Automation Letters, 4(4), 2019, 4255–4261.
  20. [20] B. Jia, Z. Hu, J. Pan, and D. Manocha, Manipulatinghighly deformable materials using a visual feedback dictionary,Proc. IEEE Int. Conf. on Robotics and Automation (ICRA),Brisbane, QLD, 2018, 239–246.
  21. [21] Z. Hu, P. Sun, and J. Pan, Three-dimensional deformable objectmanipulation using fast online gaussian process regression,IEEE Robotics and Automation Letters, 3(2), 2018, 979–986.
  22. [22] J. Zhu, Vision-based robotic manipulation of deformablelinear objects, Ph.D. Dissertation, Universite Montpellier,Montpellier, France, 2020.
  23. [23] Z. Chen, S. Li, N. Zhang, Y. Hao, and X. Zhang, Eye-to handrobotic visual tracking based on template matching on FPGAs,IEEE Access, 7(88), 2019 870–880.
  24. [24] V. Staneva and L. Younes, Modeling and estimation ofshape deformation for topology-preserving object tracking,SIAM Journal on Imaging Sciences, 7(1), 2014, 427–455.
  25. [25] Y. Hu, T.J. Carter, H.U. Ahmed, M. Emberton, C. Allen, D.J.Hawkes, and D.C. Barratt, Modelling prostate motion for datafusion during image-guided interventions, IEEE Transactionson Medical Imaging, 30(11), 2011, 1887–1900.
  26. [26] Q. Chen, Q.S. Sun, P.A. Heng, and D.S. Xia, Two stage objecttracking method based on kernel and active contour, IEEETransactions on Circuits and Systems for Video Technology,20(4), 2010, 605–609.
  27. [27] X. Cao, J. Lan, and X. Rong Li, Extension-deformationapproach to extended object tracking, Proc. 19th Int.Conf. onInformation Fusion (FUSION), Heidelberg, 2016, 1185–1192.
  28. [28] H. Joo, T. Simon, and Y. Sheikh, Total capture: A 3Ddeformation model for tracking faces, hands, and bodies, Proc.IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, June 2018, 8320–8329.
  29. [29] L. Royer, M. Marchal, A. Le Bras, G. Dardenne, and A. Krupa,Real-time tracking of deformable target in 3D ultrasoundimages, Proc. IEEE Int. Conf. on Robotics and Automation(ICRA), Seattle, WA, 2015, 2430–2435.
  30. [30] L. Royer, A. Krupa, G. Dardenne, A.L. Bras, E. Marchand,and M. Marchal, Real-time target tracking of soft tissuesin 3D ultrasound images based on robust visual informationand mechanical simulation, Medical Image Analysis, 35, 2017,582–598,
  31. [31] K. Kajihara, S. Huang, N. Bergstrom, Y. Yamakawa, and M.Ishikawa, Tracking of trajectory with dynamic deformationbased on dynamic compensation concept, Proc. IEEE Int.Conf. on Robotics and Biomimetics (ROBIO), Macau, 2017,1979–1984.
  32. [32] A. Malti, M. Taix, and F. Lamiraux, A general frameworkfor planning landmark-based motions for mobile robots,Advanced Robotics, 25(11–12), 2011, 1427–1450.
  33. [33] A. Sengupta, A. Krupa, and E. Marchand, Visual trackingof deforming objects using physics-based models, Proc.IEEE Int. Conf. on Robotics and Automation, Xi’an, 2021,14178–14184.
  34. [34] X. Feng, W. Mei, and D. Hu, A review of visual tracking withdeep learning, Proc. 2nd Int. Conf. on Artificial Intelligenceand Industrial Engineering (AIIE), Beijing, Nov. 2016,231–234.
  35. [35] S.M. Marvasti-Zadeh, L. Cheng, H. Ghanei-Yakhdan, and S.Kasaei, Deep learning for visual tracking: A comprehensivesurvey, IEEE Transactions on Intelligent TransportationSystems, 23(5), 2022, 3943–3968.
  36. [36] A. Malti, R. Hartley, A. Bartoli, and J.-H. Kim, Monoculartemplate-based 3D reconstruction of extensible surfaces withlocal linear elasticity, Proc. IEEE Conf. on Computer Visionand Pattern Recognition (CVPR), Portland, OR, 2013,1522–1529.
  37. [37] A. Malti, A. Bartoli, and R. Hartley, A linear least-squaressolution to elastic shape-from-template, Proc. IEEE Conf. onComputer Vision and Pattern Recognition (CVPR), Boston,MA, 2015, 1629–1637.
  38. [38] A. Malti and C. Herzet, Elastic shape-from-template withspatially sparse deforming forces, Proc. IEEE Conf. onComputer Vision and Pattern Recognition (CVPR), Honolulu,HI, 2017, 143–151.
  39. [39] D. Casillas-Perez, D. Pizarro, D. Fuentes-Jimenez, M. Mazo,and A. Bartoli, Equiareal shape-from template, Journal ofMathematical Imaging and Vision, 61(5), 2019, 607–626.
  40. [40] A. Ben-Israel and T.N.E. Greville, Generalized inverses: Theoryand applications. (New York, NY: Springer Science & BusinessMedia, Jun. 2003).
  41. [41] F. Saidi and A. Malti, Fast and accurate nonlinear hyper-elastic deformation with a posteriori numerical verificationof the convergence of solution: Application to the simulationof liver deformation, International Journal for NumericalMethods in Biomedical Engineering, 37(5), 2021, e3444.

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