Abstract
We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV’s checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups.
Chapter PDF
Similar content being viewed by others
Keywords
References
Chen, D., Zhang, G.: A new sub-pixel detector for x-corners in camera calibration targets. WSCG (Short Papers) 5, 97–100 (2005)
Dao, V.N., Sugimoto, M.: A robust recognition technique for dense checkerboard patterns. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3081–3084. IEEE (2010)
De la Escalera, A., Armingol, J.M.: Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration. Sensors 10(3), 2027–2044 (2010)
Fiala, M., Shu, C.: Self-identifying patterns for plane-based camera calibration. Machine Vision and Applications 19(4), 209–216 (2008)
Heikkila, J.: Geometric camera calibration using circular control points. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1066–1077 (2000)
Lucchese, L., Mitra, S.K.: Using saddle points for subpixel feature detection in camera calibration targets. In: Asia-Pacific Conference on Circuits and Systems, vol. 2, pp. 191–195. IEEE (2002)
Mallon, J., Whelan, P.F.: Which pattern? biasing aspects of planar calibration patterns and detection methods. Pattern Recognition Letters 28(8), 921–930 (2007)
Niblack, C.W., Gibbons, P.B., Capson, D.W.: Generating skeletons and centerlines from the distance transform. CVGIP: Graph. Models Image Process 54(5), 420–437 (1992)
Rufli, M., Scaramuzza, D., Siegwart, R.: Automatic detection of checkerboards on blurred and distorted images. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 3121–3126. IEEE (2008)
Scaramuzza, D.: Omnidirectional Vision: from Calibration to Root Motion Estimation. Ph.D. thesis, Swiss Federal Institute of Technology Zurich (ETHZ) (February 2008)
Sun, W., Cooperstock, J.R.: An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques. Machine Vision and Applications 17(1), 51–67 (2006)
Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses. IEEE Journal of Robotics and Automation 3(4), 323–344 (1987)
Wang, Z., Wu, W., Xu, X., Xue, D.: Recognition and location of the internal corners of planar checkerboard calibration pattern image. Applied Mathematics and Computation 185(2), 894–906 (2007)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Placht, S. et al. (2014). ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-10593-2_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10592-5
Online ISBN: 978-3-319-10593-2
eBook Packages: Computer ScienceComputer Science (R0)