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A New Linear Explicit Camera Calibration Method

새로운 선형의 외형적 카메라 보정 기법

  • Do, Yongtae (Electronic Control Engineering Major, School of Electronic and Electrical Engineering, Daegu University)
  • 도용태 (대구대학교 전자전기공학부 전자제어공학전공)
  • Received : 2013.10.28
  • Accepted : 2014.01.16
  • Published : 2014.01.29

Abstract

Vision is the most important sensing capability for both men and sensory smart machines, such as intelligent robots. Sensed real 3D world and its 2D camera image can be related mathematically by a process called camera calibration. In this paper, we present a novel linear solution of camera calibration. Unlike most existing linear calibration methods, the proposed technique of this paper can identify camera parameters explicitly. Through the step-by-step procedure of the proposed method, the real physical elements of the perspective projection transformation matrix between 3D points and the corresponding 2D image points can be identified. This explicit solution will be useful for many practical 3D sensing applications including robotics. We verified the proposed method by using various cameras of different conditions.

Keywords

References

  1. E. H. Lee, "Walk guidance technologies for the visually impaired", The Institute of Electronics and Information Engineers Magazine, vol. 32, no. 32, pp. 59-69, 2005.
  2. Y. Do, "A technique to efficiently place sensors for threedimensional robotic manipulation: For the case of stereo cameras", J. Sensor Sci. & Tech., vol. 8, no. 1, pp. 80-88, 1999.
  3. Y. Yakimovsky and R. Cunningham, "A system for extracting three-dimensional measurements from a stereo pair of TV cameras", Computer Graphics and Image Processing, vol. 7, pp. 195-210, 1978. https://doi.org/10.1016/0146-664X(78)90112-0
  4. M. Wilczkowiak, E. Boyer, and P. Sturm, "Camera calibration and 3D reconstruction from single images using parallelepipeds", Proc. IEEE Int. Conf. Computer Vision, vol. 1, pp. 142-148, 2001.
  5. S. Ganapathy, "Decomposition of transformation matrices for robot vision", Proc. IEEE Int. Conf. Robotics and Automation, pp. 130-139, 1984.
  6. L. A. Gerhardt and W. I. Kwak, "An improved adaptive stereo ranging method for three-dimensional measurements", Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 21-26, 1986.
  7. Q. Wang, L. Fu and Z. Liu, "Review on camera calibration", Proc. Chinese Control and Decision Conf., pp. 3354- 3358, 2010.
  8. J. Salvi, X. Armangue, and J. Batlle, "A comparative review of camera calibrating methods with accuracy evaluation", Pattern Recogni., vol. 35, issue 7, pp. 1617-1635, 2002. https://doi.org/10.1016/S0031-3203(01)00126-1
  9. G. Q .Wei and S. D. Ma, "Implicit and explicit camera calibration: theory and experiments", IEEE Trans. Pattern Anal. Mach. Intell., vol.16, no.5, pp. 469-480, 1994. https://doi.org/10.1109/34.291450
  10. D. M. Woo and D. C. Park, "An efficient method for camera calibration using multilayer perceptron type neural network", Proc. Int. Conf. Future Computer and Communication, pp. 358-362, 2009.
  11. M. Kim and Y. Do, "Learning the nonlinearity of a camera calibration model using GMDH algorithm", J. Sensor Sci. & Tech., vol. 14, no. 2, pp. 109-115, 2005. https://doi.org/10.5369/JSST.2005.14.2.109
  12. Y. Do, "On the neural computation of the scale factor in perspective transformation camera model", Proc. IEEE Int. Conf. Control & Automation (ICCA), pp. 418-423, 2013.
  13. R. K. Lenz and R. Y. Tsai, "Techniques for calibration of the scale factor and image center for high accuracy 3-d machine vision metrology", IEEE Trans. Pattern Anal. Mach. Intell., vol. 10, no. 5, pp. 713-720, 1988. https://doi.org/10.1109/34.6781
  14. I. Shimizu, Z. Zhang, S. Akamatsu, and K. Deguchi, "Head pose determination from one image using a generic model", Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 100-105, 1998.

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