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Automated Multiple View Inspection Based on Uncalibrated Image Sequences

  • Domingo Mery
  • Miguel Carrasco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

The Automated Multiple View Inspection (AMVI) has been recently developed for automated defect detection of manufactured objects. The approach detects defects by analysing image sequences in two steps. In the first step, potential defects are automatically identified in each image of the sequence. In the second step, the potential defects are tracked in the sequence. The key idea of this strategy is that only the existing defects (and not the false detections) can be successfully tracked in the image sequence because they are located in positions dictated by the motion of the test object. The AMVI strategy was successfully implemented for calibrated image sequences. However, it is not simple to implement it in industrial environments because the calibration process is a difficult task and unstable. In order to avoid the mentioned disadvantages, in this paper we propose a new AMVI strategy based on the tracking of potential detects in uncalibrated image sequences. Our approach tracks the potential defects based on a motion model estimated from the image sequence self. Thus, we obtain a motion model by matching structure points of the images. We show in our experimental results on aluminium die castings that the detection is promising in uncalibrated images by detecting 92.3% of all existing defects with only 0.33 false alarms per image.

Keywords

defect detection automated visual inspection multiple view geometry 

References

  1. 1.
    Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robotics and Automation 18, 890–901 (2002)CrossRefGoogle Scholar
  2. 2.
    Kita, Y., Highnam, R., Brady, M.: Correspondence between different view breast X-rays using curved epipolar lines. Computer, Vision and Understanding 83, 38–56 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Hartley, R.I., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  4. 4.
    Haralick, R., Shapiro, L.: Computer and robot vision. Addison-Wesley Publishing Co., New York (1992)Google Scholar
  5. 5.
    Castleman, K.: Digital image processing. Prentice-Hall, Englewood Cliffs (1996)Google Scholar
  6. 6.
    Mery, D.: Crossing line profile: a new approach to detecting defects in aluminium castings. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 725–732. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Mery, D., Ochoa, F., Vidal, R.: Tracking of points in a calibrated and noisy image sequence. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 647–654. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Domingo Mery
    • 1
  • Miguel Carrasco
    • 1
  1. 1.Departamento de Ciencia de la ComputaciónPontificia Universidad Católica de ChileSantiago deChile

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