Camera Calibration with Distortion Models and Accuracy Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and calibration of automated zoom lenses
Modeling and calibration of automated zoom lenses
Some Aspects of Zoom Lens Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Precise Radial Un-Distortion of Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibration and removal of lateral chromatic aberration in images
Pattern Recognition Letters
Projective rectification from the fundamental matrix
Image and Vision Computing
Efficient generic calibration method for general cameras with single centre of projection
Computer Vision and Image Understanding
Accurate chequerboard corner localisation for camera calibration
Pattern Recognition Letters
Complete generic camera calibration and modeling using spline surfaces
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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This paper provides a comparative study on the use of planar patterns in the generation of control points for camera calibration. This is an important but often neglected aspect in camera calibration. Two popular checkerboard and circular dot patterns are each examined with two detection strategies for invariance to the potential bias from projective transformations and nonlinear distortions. It is theoretically and experimentally shown that circular patterns can potentially be affected by both biasing sources. Guidelines are given to control such bias. In contrast, appropriate checkerboard detection is shown to be bias free. The findings have important implications for camera calibration, indicating that well accepted methods may give poorer results than necessary if applied naively.