Fig. 1. Chessboard target
Fig. 2. Checkerboard target
Fig. 3. Structure of blob
Fig. 4. Methodology of target extraction
Fig. 5. Circular search of pixel values based on extracted keypoint
Fig. 6. Condition of the distortion of black and white pattern
Fig. 7. Condition of the frequency of edge change
Fig. 8. Condition of the ratio of black and white pixel
Fig. 9. Procedure of identifying the number of blobs
Fig. 10. Angle calculation of polygon
Fig. 11. Determining the rectangle containing blob
Fig. 12. Identifying the blob contained within rectangle
Fig. 13. Mis-detected target
Fig. 15. Angle calculation between targets
Fig. 16. Define the direction of blobs
Fig. 17. Comparison of line length using rectangle containing blob for direction determination
Fig. 18. Labeling of targets
Fig. 19. Sequential numbering of target points
Fig. 20. Detection of rectangles containing blobs for projective transformation
Fig. 21. Result of homography transformation
Fig. 22. Location of the target from reference and template images
Fig. 23. More than one keypoint
Fig. 25. Canon EOS 800D
Fig. 26. Two types of checkerboard
Fig. 27. Captured images of checkerboard type A
Fig. 28. Captured images of checkerboard type B
Fig. 29. Area ratio of a checkerboard in the image(11%)
Fig. 30. Area ratio of a checkerboard in the image(4%)
Fig. 14. Target array in four edges
Fig. 24. one keypoint
Table 1. Camera setting
Table 2. Detection rate of checkerboard targets
References
- Kwon, S.I. and Kim, E.M. (2018), Blob configuration for target estimation of multi-camera checker board images, 2018 Joint fall conference, 18 November 2018, Jeju, Korea, pp. 50-51.
- Fursattel, P., Dotenco, S., Placht, S., Balda, M., Maier, A., and Riess, C. (2016), OCPAD - Occluded checkerboard pattern detector, 2016 IEEE Winter Conference on Applications of Computer Vision, 7-10 March 2016, NY, USA, pp. 1-9.
- Lari, Z., Habib, A., Mazaheri, M., and Al-Durgham, K. (2013), Multi-camera system calibration with built-in relative orientation constraints(part 2) - Automation, implementation, and experimental results, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 3, pp. 205-216. https://doi.org/10.7848/ksgpc.2014.32.3.205
- Habib, A., Lari, Z., Kwak, E., and Al-Durgham, K. (2013), Automated detection, localization, and identification of signalized targets and their impact on digital camera calibration, Revista Brasileira de Cartografia, Vol. 65, No. 4, pp. 785-803.
- Oh, S.Y. and Cho, N.I. (2016), Finding locating checker board using corner detection and interpolation, 2016 Conference of The Korean Society Of Broad Engineers, The Korean Society Of Broad Engineers, 4 November 2016, Seoul, Korea, pp. 165-168.
- Park, J.M., Lee, J.I., Cho, J.B., and Lee, J.W. (2015), Precise detection of coplanar checkerboard corner points for stereo camera calibration using a single frame, Journal of Institute of Control, Robotics and Systems, Vol. 21, No. 7, pp. 602-608. (in Korean with English abstract) https://doi.org/10.5302/J.ICROS.2015.15.0011
- Placht, S., Mengue, E.A., Hofmann, H., Schaller, C., and Balda, M. (2014), ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration, 2014 European Conference on Computer Vision, 6-12 September 2014, Zurich, Switzerland, pp. 766-779.
- Rufli, M., Scaramuzza, D., and Siegwart, R. (2008), Automatic detection of checkerboards on blurred and distorted images, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Intelligent Robots and Systems, 22-26 September 2008, Nice, France, pp. 3121-3126.
- Yu, Y.J. (2015), Automatic Checkerboard Corner Detection for Camera Calibration, Master's thesis, Dongkuk University, Seoul, Korea, 49p.
Cited by
- 체커보드의 유형에 따른 스테레오 카메라 캘리브레이션의 정확도 비교 vol.38, pp.6, 2020, https://doi.org/10.7848/ksgpc.2020.38.6.511
- 근거리 사진측량을 위한 스테레오 카메라의 안정성 분석 vol.39, pp.3, 2018, https://doi.org/10.7848/ksgpc.2021.39.3.123