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Study of Strength Tests with Computer Vision Techniques

  • Alvaro Rodriguez
  • Juan R. Rabuñal
  • Juan L. Perez
  • Fernando Martinez-Abella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

Knowing the strain response of materials in strength tests is one of the main issues in construction and engineering fields. In these tests, information about displacements and strains is usually carried out using physical devices attached to the material.

In this paper, the suitability of Computer Vision techniques to analyse strength tests without interfering with the assay is discussed and a new technique is proposed.

This technique measures displacements and deformations from a video sequence of the assay.

With this purpose a Block-Matching Optical Flow algorithm is integrated with a calibration process to extract the vectorfield from the displacement in the material.

To evaluate the proposed technique, a synthetic image set and a real sequence from a strength tests were analysed.

Keywords

Computer Vision Optical Flow Block-Matching Strength Tests 

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References

  1. 1.
    Abad, F.H., Abad, V.H., Andreu, J.F., Vives, M.O.: Application of Projective Geometry to Synthetic Cameras. In: XIV International Conference of Graphic Engineering (2002)Google Scholar
  2. 2.
    Amiaz, T., Lubetzky, E., Kiryati, N.: Coarse to over-fine optical flow estimation. Pattern Recognition 40(9), 1503–2494 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Austvoll, I.: A Study of the Yosemite Sequence Used as a Test Sequence for Estimation of Optical Flow. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 659–668. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Chin, R.T., Harlow, C.A.: Automated visual ispection. IEEE Transactios on Pattern Analysis ad Machine Intelligence 4(6) (1982)Google Scholar
  5. 5.
    Chivers, K., Clocksin, W.: Inspection of Surface Strain in Materials Using Optical Flow. In: British Machine Vision Conference 2000, pp. 392–401 (2000)Google Scholar
  6. 6.
    Deng, Z., Richmond, M.C., Guensch, G.R., Mueller, R.P.: Study of Fish Response Using Particle Image Velocimetry and High-Speed, High-Resolution Imaging. Technical Report. PNNL-14819 (2004)Google Scholar
  7. 7.
    Graphics and Vision Research Laboratory, Department of Computer Science, University of Otago, http://www.cs.otago.ac.nz (accessed November 2010)
  8. 8.
    Heeger, D.: Model for the extraction of image flow. Journal of the Optical Society of America A: Optics, Image Science, and Vision 4, 1455–1471 (1987)CrossRefGoogle Scholar
  9. 9.
    Horn, B.K.P., Schunk, B.G.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  10. 10.
    Kadem, L.: Particle Image Velocimetry for Fluid Dynamics Measurements. Applied Cardiovascular Fluid Dynamics (Concordia University), Particle Image Velocimetry (2008)Google Scholar
  11. 11.
    Malsch, U., Thieke, C., Huber, P.E., Bendl, R.: An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy. Phys. Med. Biol. 51, 4789–4806 (2006)CrossRefGoogle Scholar
  12. 12.
    Manchado, A.R.: Calibracion de camaras no metricas por el metodo de las lineas rectas. Mapping 51, 74–80 (1999)Google Scholar
  13. 13.
    Martin, N., Perez, B.A., Aguilera, D.G., Lahoz, J.G.: Applied Analysis of Camera Calibration Methods for Photometric Uses. In: VII National Conference of Topography and Cartography (2004)Google Scholar
  14. 14.
    McCane, B., Novins, K., Crannitch, D., Galvin, B.: On Benchmarking Optical Flow. Computer Vision and Image Understanding 84, 126–143 (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Open Source Computer Vision, http://opencv.willowgarage.com (accessed November 2010)
  16. 16.
    Particle image Velocimetry, http://www.piv.de (accessed November 2010)
  17. 17.
    Raffel, M., Willert, C., Kompenhans, J.: Particle Image Velocimetry, a Practical Guide. Springer, Berlin (2000)Google Scholar
  18. 18.
    Raffel, M., Willert, C., Kompenhans, J.: Particle Image Velocimetry, a Practical Guide, 2nd edn. Springer, Berlin (2007)Google Scholar
  19. 19.
    Scharstein, D., Baker, S., Lewis, J.P.: A database and evaluation methodology for Optical Flow. In: ICCV (2007)Google Scholar
  20. 20.
    Schwarz, D., Kasparek, T.: Multilevel Block Matching technique with the use of Generalized Partial Interpolation for Nonlinear Intersubject Registration of MRI Brain Images. European Journal for Biomedical Informatics 1, 90–97 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alvaro Rodriguez
    • 1
  • Juan R. Rabuñal
    • 1
    • 2
  • Juan L. Perez
    • 1
  • Fernando Martinez-Abella
    • 2
  1. 1.Dept. of Information and Communications TechnologiesUniversity of A CoruñaA CoruñaSpain
  2. 2.Centre of Technological Innovation in Construction and Civil Engineering (CITEEC)University of A CoruñaA CoruñaSpain

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