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A Deformable Object Tracking Algorithm Based on the Boundary Element Method that is Robust to Occlusions and Spurious Edges

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Abstract

The manipulation of deformable objects is an important problem in robotics and arises in many applications including biomanipulation, microassembly, and robotic surgery. For some applications, the robotic manipulator itself may be deformable. Vision-based deformable object tracking can provide feedback for these applications. Computer vision is a logical sensing choice for tracking deformable objects because the large amount of data that is collected by a vision system allows many points within the deformable object to be tracked simultaneously. This article introduces a template based deformable object tracking algorithm, based on the boundary element method, that is able to track a wide range of deformable objects. The robustness of this algorithm to occlusions and to spurious edges in the source image is also demonstrated. A robust error measure is used to handle the problem of occlusion and an improved edge detector based on the Canny edge operator is used to suppress spurious edges. This article concludes by quantifying the performance increase provided by the robust error measure and the robust edge detector. The performance of the algorithm is also demonstrated through the tracking of a sequence of cardiac MRI images.

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References

  • Beer, G. (2001). Programming the boundary element method. New York: Wiley.

    Google Scholar 

  • Canny, J. (1986). A Computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6).

  • Draper, N. R. (1998). Applied regression analysis (3rd ed.). New York: Wiley.

    MATH  Google Scholar 

  • Fletcher, R. (1987). Practical methods of optimization. New York: Wiley.

    MATH  Google Scholar 

  • Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Booth, M., & Rossi, F. (2001). GNU scientific library reference manual. Bristol: Network Theory Limited.

    Google Scholar 

  • Greminger, M. A., & Nelson, B. J. (2004). Vision-based force measurement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3), 290–298.

    Article  Google Scholar 

  • Jaswon, M. A., & Symm, G. T. (1977). Integral equation methods in potential theory and elastostatics. New York: Academic.

    MATH  Google Scholar 

  • Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: active contour models. International Journal of Computer Vision, 321–331.

  • McInerney, T., & Terzopoulos, D. (1993). A finite element model for 3D shape reconstruction and nonrigid motion tracking. In Proceedings fourth IEEE international conference on computer vision (pp. 518–523), April 1993.

  • Metaxas, D. (1997). Physics-based deformable models. Boston: Kluwer Academic.

    Google Scholar 

  • Nakamura, Y., Kishi, K., & Kawakami, H. (2001). Heartbeat synchronization for robotic cardiac surgery. In IEEE international conference on robotics and automation (ICRA2001) (Vol. 2, pp. 2014–2019). Seoul, Korea, May 2001.

  • Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1993). Numerical recipes in C: the art of scientific computing. Cambridge: Cambridge University Press.

    Google Scholar 

  • Rizzo, F. J. (1967). An integral equation approach to boundary value problems of classical elastostatics. Quarterly Applied Mathematics, 25, 83–95.

    MATH  Google Scholar 

  • Sokolnikoff, I. (1983). Mathematical theory of elasticity. Malabar: Krieger.

    MATH  Google Scholar 

  • Stewart, C. V. (1999). Robust parameter estimation in computer vision. SIAM Review, 41(3), 513–537.

    Article  MATH  MathSciNet  Google Scholar 

  • Sun, Y., & Nelson, B. J. (2002). Biological cell injection using an autonomous microrobotic system. The International Journal of Robotics Research (IJRR), 21(10–11), 861–868.

    Article  Google Scholar 

  • Tsap, L., Goldgof, D., & Sarkar, S. (1998). Efficient nonlinear finite element modeling of nonrigid objects via optimization of mesh models. Computer Vision and Image Understanding, 69, 330–350.

    Article  Google Scholar 

  • Wang, X., Ananthasuresh, G. K., & Ostrowski, J. P. (2001). Vision-based sensing of forces in elastic objects. Sensors and Actuators A-Physical, 94(3), 142–156.

    Article  Google Scholar 

  • Yuille, A., Cohen, D., & Hallinan, W. (1992). Feature extraction from faces using deformable templates. International Journal of Computer Vision, 8(2), 99–111.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Michael A. Greminger

  2. Institute of Robotics and Intelligent Systems, Swiss Federal Institute of Technology (ETH), 8092, Zurich, Switzerland

    Bradley J. Nelson

Authors
  1. Michael A. Greminger
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  2. Bradley J. Nelson
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Correspondence to Michael A. Greminger.

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Greminger, M.A., Nelson, B.J. A Deformable Object Tracking Algorithm Based on the Boundary Element Method that is Robust to Occlusions and Spurious Edges. Int J Comput Vis 78, 29–45 (2008). https://doi.org/10.1007/s11263-007-0076-6

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  • DOI: https://doi.org/10.1007/s11263-007-0076-6

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