JRM Vol.27 No.2 pp. 208-214
doi: 10.20965/jrm.2015.p0208


Recognition of Center Circles for Encoded Targets in Digital Close-Range Industrial Photogrammetry

Huang Xuemei, Su Xinyong, and Liu Weihong

College of Mechanical Engineering, Shandong University of Technology
No.12 Zhangzhou Road, Zhangdian district, Zibo city, Shandong province 255049, China

November 5, 2014
January 27, 2015
April 20, 2015
digital close-range industrial photogrammetry, encoded target, recognition
Final position of center circles

Recognition of encoded targets on the industrial backgrounds are hot topics in digital close-rang industrial photogrammetry. To recognize the encoded targets, the center circles of targets need to be detected above all. A method was proposed to spot the center circles accurately based on some criteria put forward by authors and was realized by the program developed in MatlabR2010a. Firstly, according to the image that was preprocessed by image-binary threshold and Canny edge detection, straight lines in image were deleted and dimension criteria was applied to obtain possible contours of center circles. Then all these contours were fitted to ellipses based on the Least Square Algorithm and the candidate ellipses were remained except those that did not meet the shape criteria. Finally because of shooting error, all candidate ellipses were modified to obtain the positions of encoded targets’ center circles precisely. In laboratory, an experiment was conducted to test and verify the theory mentioned above and the result showed the robust of the method in this paper.

Cite this article as:
H. Xuemei, S. Xinyong, and L. Weihong, “Recognition of Center Circles for Encoded Targets in Digital Close-Range Industrial Photogrammetry,” J. Robot. Mechatron., Vol.27, No.2, pp. 208-214, 2015.
Data files:
  1. [1] R. B. Xia, J. B. Zhao, W. J. Liu, J. H. Wu, S. P. Fu, J. Jiang et al., “A robust recognition algorithm for encoded targets in close-range photogrammetry,” J. of Information Science and Engineering, Vol.8, No.2, pp. 407-418, 2012.
  2. [2] E. M. Mikhail, M. L. Akey, and O. R. Mitchell, “Detection and sub-pixel location of photogrammetric targets in digital images,” Pattern Recognit in Photogramm, Sel Pap of the Spec Workshop, Vol.39, No.3, pp. 63-83, Graz, Austria, September 1983.
  3. [3] S. J. Ahn and W. Rauh, “Circular coded targets for automation of optical 3D-measurement and camera calibration,” Int. J. of Pattern Recognition and Artificial Intelligence, Vol.15, No.6, pp. 905-919, 2001.
  4. [4] T. Tasaki, S. Tokura, T. Sonoura, F. Ozaki, and N. Matsuhira, “Obstacle location classification and self-localization by using a mobile omnidirectional camera based on tracked floor boundary points and tracked scale-rotation invariant feature points,” J. of Robotics and Mechatronics, Vol.23, No.6, pp. 1012-1013, 2011.
  5. [5] C. Wang, M. Dong, N. Lv, and L. Zhu, “New encoded Method of measurement targets and its recognition algorithm,” Tool Technology, Vol.17, No.4, pp. 26-30, 2002.
  6. [6] M. Xia and B. Liu, “Image registration by super curves,” IEEE Trans. on Image Processing, Vol.13, No.5, pp. 72-81, April 2004.
  7. [7] X. J. Zheng, J. J. Wang, and C. Zuo, “Geometric feature based information process of marks in photogrammetry,” Test and Measurement Technology. Vol.34, No.5, pp. 49-52, 2007.
  8. [8] Z. P. Chen, David T. W. Chan, Z. L. Ye, and G. H. Peng, “Target recognition based on mathematical morphology,” Proc. of 2007 10th IEEE Int. Conf. on Computer Aided Design and Computer Graphics, Beijing, China, pp. 457-460, October 2007.
  9. [9] H. W. Chen, V. Chris, T. Michael, and S. Steve, “Robust extended target detection using non-linear morphological operations,” Proc. of SPIE – The Int. Society for Optical Engineering, Orlando, FL, United states, Vol.7694, pp. 452-462, April 2010.
  10. [10] F. Mina, M. Ali, and A. M. Reza, “Detection of small target based on morphological filters,” ICEE2012 – 20th Iranian Conf. on Electrical Engineering, Tehran, Iran, pp. 1097-1101, May 2012.
  11. [11] F. A. Tsai and H. A. Chang, “Detection of vanishing points using Hough transform for single view 3D reconstruction,” 34th Asian Conf. on Remote Sensing, Vol.2, pp. 1182-1189, October 2013.

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Last updated on Nov. 12, 2018