X-Corner Detection for Camera Calibration Using Saddle Points
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
X-Corner Detection for Camera Calibration Using Saddle Points

Authors: Abdulrahman S. Alturki, John S. Loomis

Abstract:

This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: Camera Calibration, Corner Detector, Saddle Points, X-Corners.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1112238

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3082

References:


[1] R. Y. Tsai, “An efficient and accurate camera calibration technique for 3d machine vision,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1986, 1986.
[2] G.-Q. Wei and S. Ma, “A complete two-plane camera calibration method and experimental comparisons,” in Computer Vision, 1993. Proceedings., Fourth International Conference on. IEEE, 1993, pp. 439–446.
[3] J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 10, pp. 965–980, 1992.
[4] Z. Zhang, “A flexible new technique for camera calibration,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 11, pp. 1330–1334, 2000.
[5] J.-Y. Bouguet, “Camera calibration toolbox for matlab,” 2004.
[6] Z. Wang, W. Wu, X. Xu, and D. Xue, “Recognition and location of the internal corners of planar checkerboard calibration pattern image,” Applied mathematics and computation, vol. 185, no. 2, pp. 894–906, 2007.
[7] G. Wang, H.-T. Tsui, Z. Hu, and F. Wu, “Camera calibration and 3d reconstruction from a single view based on scene constraints,” Image and Vision Computing, vol. 23, no. 3, pp. 311–323, 2005.
[8] G. Wang, H.-T. Tsui, and Q. J. Wu, “What can we learn about the scene structure from three orthogonal vanishing points in images,” Pattern Recognition Letters, vol. 30, no. 3, pp. 192–202, 2009.
[9] S. Placht, P. F¨ursattel, E. A. Mengue, H. Hofmann, C. Schaller, M. Balda, and E. Angelopoulou, “Rochade: Robust checkerboard advanced detection for camera calibration,” in Computer Vision–ECCV 2014. Springer, 2014, pp. 766–779.
[10] L. Lucchese and S. K. Mitra, “Using saddle points for subpixel feature detection in camera calibration targets,” in Circuits and Systems, 2002. APCCAS’02. 2002 Asia-Pacific Conference on, vol. 2. IEEE, 2002, pp. 191–195.
[11] H. P. Moravec, “Towards automatic visual bbstacle avoidance,” in International Conference on Artificial Intelligence (5th: 1977: Massachusetts Institute of Technology), 1977.
[12] C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors,” International Journal of computer vision, vol. 37, no. 2, pp. 151–172, 2000.
[13] S. M. Smith and J. M. Brady, “Susana new approach to low level image processing,” International journal of computer vision, vol. 23, no. 1, pp. 45–78, 1997.
[14] X. Gao, F. Sattar, and R. Venkateswarlu, “Multiscale corner detection of gray level images based on log-gabor wavelet transform,” Circuits and Systems for Video Technology, IEEE Transactions on, vol. 17, no. 7, pp. 868–875, 2007.
[15] A. Willis and Y. Sui, “An algebraic model for fast corner detection,” in Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009, pp. 2296–2302.
[16] D. Chen and G. Zhang, “A new sub-pixel detector for x-corners in camera calibration targets,” 2005.
[17] D. Parks and J.-P. Gravel, “Corner detection,” URL http://www. cim. mcgill. ca/˜ dparks/CornerDetector/harris. ht, 2004.
[18] R. Jain, R. Kasturi, and B. G. Schunck, Machine vision. McGraw-Hill New York, 1995, vol. 5.