Recognition and location of the internal corners of planar checkerboard calibration pattern image

The recognition and location of the internal corners of a planar checkerboard calibration pattern image is very important to camera calibration. An effective approach is proposed to automatically recognize and locate 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. Experiments show that compared with the commonly used interactive method, 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]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

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

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

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

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

[6]  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..

[7]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

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

[9]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

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

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

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

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

[14]  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.

[15]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognit. Lett..

[16]  Andrew Zisserman,et al.  Multiple view geometry in computer visiond , 2001 .

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

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

[19]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..

[20]  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).

[21]  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.