Artificial Intelligence - Special volume on computer vision
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical evaluation of a calibration chart detector
Machine Vision and Applications
ARTag, a Fiducial Marker System Using Digital Techniques
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Parameter-Free Radial Distortion Correction with Centre of Distortion Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
High resolution passive facial performance capture
ACM SIGGRAPH 2010 papers
Comparative studies of parallel and vertical stereo vision-based 3D pneumatic arms
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
The matching of infrared markers for tracking objects using stereo pairs
Pattern Recognition and Image Analysis
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Determining camera calibration parameters is a time-consuming task despite the availability of calibration algorithms and software. A set of correspondences between points on the calibration target and the camera image(s) must be found, usually a manual or manually guided process. Most calibration tools assume that the correspondences are already found. We present a system which allows a camera to be calibrated merely by passing it in front of a panel of self-identifying patterns. This calibration scheme uses an array of fiducial markers which are detected with a high degree of confidence, each detected marker provides one or four correspondence points. Experiments were performed calibrating several cameras in a short period of time with no manual intervention. This marker-based calibration system was compared to one using the OpenCV chessboard grid finder which also finds correspondences automatically. We show how our new marker-based system more robustly finds the calibration pattern and how it provides more accurate intrinsic camera parameters.