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Depth Measurement of Materials Attached to Cylinder Using Line Laser

라인 레이저를 이용한 원통 부착물의 심도 측정

  • Kim, Yongha (Department of Electrical Information Control Engineering, Hongik Univ.) ;
  • Ko, Kwangjin (Department of Electrical Information Control Engineering, Hongik Univ.) ;
  • Yeon, Sungho (Department of Electrical Information Control Engineering, Hongik Univ.) ;
  • Kim, Jaemin (School of Electronic and Electrical Engineering, Hongik Univ.)
  • Received : 2017.01.09
  • Accepted : 2017.01.25
  • Published : 2017.02.28

Abstract

Line-laser beams are used for accurate measurement of 3D shape, which is robust to external illumination. For depth measurement, we project a line-laser beam across an object from the face and take an image of the beam on the object surface using a CCD camera at some angle with respect to the face. For shape measurement, we project parallel line-laser beams with narrow line to line distance. When a layer of thin materials attached to a cylinder is long narrow along its circumference, we can measure the shape of the layer with a small number of parallel line beams if we project line beams along the circumference of the cylinder. Measurement of the depth of the attached materials on a line-laser beam is based on the number of pixels between an imaginary line along the imaginary cylinder without the attached materials and the beam line along the materials attached to the cylinder. For this we need to localize the imaginary line in the captured image. In this paper, we model the shape of the line as an ellipse and localize the line with least square estimate. The proposed method results in smaller error (maximum 0.24mm) than a popular 3D depth camera (maximum 1mm).

Keywords

References

  1. S. Shimizu, T. Kondo, T. Kohashi, M. Tsuruta, and T. Komuro, "A New Algorithm for Exposure Control based on Fuzzy Logic for Video Cameras," Journal of IEEE Transactions on Consumer Electronics, Vol. 38, pp. 617-623, 1992. https://doi.org/10.1109/30.156745
  2. W. Huang and R. Kovacevic "A Laser-based Vision System for Weld Quality Inspection," Journal of Sensors, Vol. 11, pp. 506-521, 2011. https://doi.org/10.3390/s110100506
  3. Specification of 3D Webcam Sensor, http://www.artcreaxtion.com/3d-depth-sensor/apple-primesense/primsense-carmine-1-09- 3d-webca.html (accessed Jan., 06, 2017).
  4. J. Forest, J. Salvi, E. Cabruja, and C. Pous, "Laser Stripe Peak Detector for 3D Scanners, A FIR Filter Approach," Proceedings of 17th International Conference on Pattern Recognition, pp. 646-649, 2004.
  5. M. Nixon and A. Aguado, Feature Extraction & Image Processing for Computer Vision, Academic Press, New York, 2012.
  6. T. Strutz, Data Fitting and Uncertainty (A Practical Introduction to Weighted Least Squares and Beyond) , Springer Vieweg, Wiesbaden, 2016.
  7. B.A. Ahlborn, D. Thompson, O. Kreylos,B. Hamann, B. Hamann, and O. Staadt, "A Practical System for Laser Pointer Interaction on Large Displays," Proceeding of the ACM Symposium, Virtual Reality Software and Technology, pp. 106-109, 2005.
  8. M.E. Latoschik and E. Bomberg, "Augmenting a Laser Pointer with a Diffraction Grating for Monoscopic 6dof Detection," Journal of Virtual Reality and Broadcasting, Vol. 4, No. 14, pp. 1-9, 2007.
  9. S. Yeon, J. Kim, "Robust Illumination Change Detection Using Image Intensity and Texture," Journal of Korea Multimedia Society, Vol. 16, No. 2, 169-179, 2013. https://doi.org/10.9717/kmms.2013.16.2.169
  10. E.R. Dougherty and R.A. Ltufo, Hands-on Morphological Image Processing, SPIE Press, Bellingham, Washington, 2003.
  11. Precision Standard Grade of LM Guide, http://pmikorea.mireene.com/skin_build61/sub_page.php?page_idx=138 (accessed Jan., 06, 2017).