Extraction of the corner of checkerboard image

The extraction of the internal corners of planar checkerboard pattern image is of vital importance to camera calibration, for the accuracy of camera calibration depends on the accuracy of it. An effective approach is proposed to automatically extract the internal corners of the planar checkerboard calibration pattern image based on the characteristics of local intensity and the grid line architecture of the planar checkerboard pattern image. The proposed procedure consists of the detection of image corners, the recognition of the corners at the intersections of black and white squares and the recognition of the corners at the intersections of two groups of grid lines. The proposed approach obviously reduces the time cost for camera calibration, speeds up calibration process and is especially adapted for automatic calibration based on multiple images.

[1]  Olivier D. Faugeras,et al.  A theory of self-calibration of a moving camera , 1992, International Journal of Computer Vision.

[2]  Jana Kosecka,et al.  Efficient computation of vanishing points , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[3]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[4]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[5]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognition Letters.

[6]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  H. Opower Multiple view geometry in computer vision , 2002 .

[9]  Stephen J. Maybank,et al.  On plane-based camera calibration: A general algorithm, singularities, applications , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[11]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  G. F. McLean,et al.  Vanishing Point Detection by Line Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Songde Ma,et al.  A complete two-plane camera calibration method and experimental comparisons , 1993, 1993 (4th) International Conference on Computer Vision.

[14]  O. Faugeras Stratification of three-dimensional vision: projective, affine, and metric representations , 1995 .

[15]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[16]  Brian James O'Kennedy,et al.  Stereo camera calibration , 2002 .

[17]  W. Faig CALIBRATION OF CLOSE-RANGE PHOTOGRAMMETRIC SYSTEMS: MATHEMATICAL FORMULATION , 1975 .

[18]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..