DOI QR코드

DOI QR Code

Parameter Calibration of Laser Scan Camera for Measuring the Impact Point of Arrow

화살 탄착점 측정을 위한 레이저 스캔 카메라 파라미터 보정

  • 백경동 (부산대학교 전자전기공학과) ;
  • 천성표 (영진전문대학 신재생에너지전기계열) ;
  • 이인성 (부산대학교 전자전기공학과) ;
  • 김성신 (부산대학교 전자전기공학부)
  • Received : 2011.06.29
  • Accepted : 2011.10.21
  • Published : 2012.02.15

Abstract

This paper presents the measurement system of arrow's point of impact using laser scan camera and describes the image calibration method. The calibration process of distorted image is primarily divided into explicit and implicit method. Explicit method focuses on direct optical property using physical camera and its parameter adjustment functionality, while implicit method relies on a calibration plate which assumed relations between image pixels and target positions. To find the relations of image and target position in implicit method, we proposed the performance criteria based polynomial theorem model that overcome some limitations of conventional image calibration model such as over-fitting problem. The proposed method can be verified with 2D position of arrow that were taken by SICK Ranger-D50 laser scan camera.

Keywords

References

  1. Wang, J., Shi, F., Zhang, J., and Liu, Y., 2008, "A New Calibration Model of Camera Lens Distortion," Pattern Recognition, Vol. 41, No. 2, pp. 607-615. https://doi.org/10.1016/j.patcog.2007.06.012
  2. Shin, D. S., and Chung S. C., 2011, "2.5D Quick Turnaround Engraving System through Recognition of Boundary Curves in 2D Images," Korean Society of Manufacturing Technology Engineering, Vol. 20, No. 4, pp. 369-375.
  3. Jeong, K., and Ahn, K. U., 2010, "Inspection of the Knuckle Bracket Holes of a Shock-absorber using Image Processing Method," Korean Society of Manufacturing Technology Engineering, Vol. 19, No. 6, pp. 768-775.
  4. Lee, J. H., 2010, "A Study on Interpolation Algorithm to Improve the Blurring of Magnified Image," Korean Society of Manufacturing Technology Engineering, Vol. 19, No. 4, pp. 562-569.
  5. Roh, S. B., Oh, S. K., and Pedrycz, W., 2010, "A Fuzzy Ensemble of Parallel Polynomial Neural Networks with Information Granules Formed by Fuzzy Clustering," Knowledge-Based Systems, Vol. 23, No. 3, pp. 202-219. https://doi.org/10.1016/j.knosys.2009.12.002
  6. Yu, W., 2004, "Image-based Lens Geometric Distortion Correction using Minimization of Average Bicoherence Index," Pattern Recognition, Vol. 37, No. 6, pp. 1175-1187. https://doi.org/10.1016/j.patcog.2004.01.001
  7. Bai, Y., and Wang D., 2010, "On the Comparison of Trilinear, Cubic Spline, and Fuzzy Interpolation Methods in the High-accuracy Measurements," IEEE Trans. on Fuzzy Systems, Vol. 18, No. 5, pp. 1016-1022. https://doi.org/10.1109/TFUZZ.2010.2064170

Cited by

  1. Hardware Configuration and Paradox Measurement for the Determination of Arrow Trajectory vol.21, pp.3, 2012, https://doi.org/10.7735/ksmte.2012.21.3.459
  2. Measurement of Archer's Paradox Size using Multiple Frames vol.23, pp.1, 2014, https://doi.org/10.7735/ksmte.2014.23.1.021
  3. Mamdani 퍼지추론을 이용한 화살의 탄착점 측정 시스템 vol.22, pp.4, 2012, https://doi.org/10.5391/jkiis.2012.22.4.521