Publisher : The Korean Institute of Broadcast and Media Engineers
DOI : 10.5909/JBE.2016.21.1.36
Title & Authors
Robust Semi-auto Calibration Method for Various Cameras and Illumination Changes Shin, Dong-Won; Ho, Yo-Sung;
Recently, many 3D contents have been produced through the multiview camera system. In this system, since a difference of the viewpoint between color and depth cameras is inevitable, the camera parameter plays the important role to adjust the viewpoint as a preprocessing step. The conventional camera calibration method is inconvenient to users since we need to choose pattern features manually after capturing a planar chessboard with various poses. Therefore, we propose a semi-auto camera calibration method using a circular sampling and an homography estimation. Firstly, The proposed method extracts the candidates of the pattern features from the images by FAST corner detector. Next, we reduce the amount of the candidates by the circular sampling and obtain the complete point cloud by the homography estimation. Lastly, we compute the accurate position having the sub-pixel accuracy of the pattern features by the approximation of the hyper parabola surface. We investigated which factor affects the result of the pattern feature detection at each step. Compared to the conventional method, we found the proposed method released the inconvenience of the manual operation but maintained the accuracy of the camera parameters.
camera calibration;pattern feature detection;circular sampling;homography estimation;
C. Fehn, "A 3D-TV approach using depth-image-based rendering (DIBR)," Proc. of 3rd IASTED Conference on Visualization, Imaging, and Image Processing, pp. 482-487, 2003.
Z. Zhang, "A Flexible New Technique for Camera Calibration," Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000.
D. Shin and Y. Ho, "Pattern Feature Detection for Camera Calibration using Circular Sample Pixel," Proc. of 2015 Korean Society of Broadcast Engineers Summer Conference, vol. 2015, no. 7, pp. 433-434, 2015.
E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," Proc. of the 9th European conference on Computer Vision, Berlin, Heidelberg, vol. Part I, no. 34, pp. 430-443, 2006.
R. Hartley, A. Zisserman, "Multiple View Geometry in Computer Vision," Cambridge University Press, Cambridge, pp. 200-204, 2003.
L. Lucchese and S. K. Mitra, "Using saddle points for subpixel feature detection in camera calibration targets," Circuits and Systems, vol. 2, no 2, pp. 191-195, 2002.