Camera calibration: active versus passive targets

Traditionally, most camera calibrations rely on a planar target with well-known marks. However, the localization error of the marks in the image is a source of inaccuracy. We propose the use of high-resolution digital displays as active calibration targets to obtain more accurate calibration results for all types of cameras. The display shows a series of coded patterns to generate correspondences between world points and image points. This has several advantages. No special calibration hardware is necessary because suitable displays are practically ubiquitious. The method is fully automatic, and no identification of marks is necessary. For a coding scheme based on phase shifting, the localization accuracy is approximately independent of the camera's focus settings. Most importantly, higher accuracy can be achieved compared to passive targets, such as printed checkerboards. A rigorous evaluation is performed to substantiate this claim. Our active target method is compared to standard calibrations using a checkerboard target. We perform camera, calibrations with different combinations of displays, cameras, and lenses, as well as with simulated images and find markedly lower reprojection errors when using active targets. For example, in a stereo reconstruction task, the accuracy of a system calibrated with an active target is five times better.

[1]  Roger Mohr,et al.  Accuracy in image measure , 1994, Other Conferences.

[2]  Timothy A. Clarke,et al.  Comparison of some techniques for the subpixel location of discrete target images , 1994, Other Conferences.

[3]  Kunihiro Chihara,et al.  High-Accuracy and Robust Localization of Large Control Markers for Geometric Camera Calibration , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  G Sansoni,et al.  Three-dimensional vision based on a combination of gray-code and phase-shift light projection: analysis and compensation of the systematic errors. , 1999, Applied optics.

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

[6]  Robert A. Schowengerdt,et al.  Effect of point-spread functions on precision edge measurement , 1994 .

[7]  Joaquim Salvi,et al.  A state of the art in structured light patterns for surface profilometry , 2010, Pattern Recognit..

[8]  Axel Pinz,et al.  Subpixel Corner Detection for Tracking Applications using CMOS Camera Technology , 2002 .

[9]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  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..

[11]  Andrea Torsello,et al.  Robust Camera Calibration using Inaccurate Targets , 2010, BMVC.

[12]  Aubrey K. Dunne,et al.  A Comparison of New Generic Camera Calibration with the Standard Parametric Approach , 2007, MVA.

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

[14]  Shree K. Nayar,et al.  The Raxel Imaging Model and Ray-Based Calibration , 2005, International Journal of Computer Vision.

[15]  Janne Heikkilä,et al.  Geometric Camera Calibration Using Circular Control Points , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  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.

[17]  Steven A. Shafer,et al.  What is the center of the image? , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Chang Shu,et al.  Fully Automatic Camera Calibration Using Self-Identifying Calibration Targets * , 2005 .

[19]  Paul F. Whelan,et al.  Which pattern? Biasing aspects of planar calibration patterns and detection methods , 2007, Pattern Recognit. Lett..

[20]  C. Fraser,et al.  Digital camera calibration methods: Considerations and comparisons , 2006 .

[21]  Elsayed E. Hemayed,et al.  A survey of camera self-calibration , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[22]  P. Sturm,et al.  Theory and Experiments towards Complete Generic Calibration , 2005 .

[23]  Sing Bing Kang,et al.  Parameter-Free Radial Distortion Correction with Center of Distortion Estimation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Shree K. Nayar,et al.  A general imaging model and a method for finding its parameters , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[25]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[26]  Peter F. Sturm,et al.  Calibration of Cameras with Radially Symmetric Distortion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Yasushi Yagi,et al.  Calibration of lens distortion by structured-light scanning , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Sanjit K. Mitra,et al.  Using saddle points for subpixel feature detection in camera calibration targets , 2002, Asia-Pacific Conference on Circuits and Systems.

[29]  Gary R. Bradski,et al.  Learning OpenCV - computer vision with the OpenCV library: software that sees , 2008 .

[30]  D. Lau,et al.  Gamma model and its analysis for phase measuring profilometry. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  Guangjun Zhang,et al.  A New Sub-Pixel Detector for X-Corners in Camera Calibration Targets , 2005, WSCG.