Extrinsic parameters and focal length calibration using rotation-symmetric patterns

The authors present a camera calibration method for estimating the extrinsic parameters and focal length of a camera using only a single image of a planar rotation-symmetric pattern. The frontal parallel image of the pattern can preserve the rotation-symmetric pattern, causing the corresponding frieze-expansion (FE) pattern to be expressed as a low-rank matrix, which does not exist when the image is captured from an oblique view. The proposed method derives the forward formulation between the FE of the image observed from an oblique view and the low-rank, FE pattern of the frontal parallel image. An optimisation problem arises when rank minimisation techniques are introduced; by solving this problem, one can obtain the camera parameters relative to the front view. Simultaneously, the rotation-symmetric texture is recovered as a by-product. This new method takes advantage of the raw intensity values of the observed image itself, bypassing the need to extract any low-level features and simplifying human intervention. Experimental results demonstrate the validity of the proposed method.

[1]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[2]  Shengyong Chen,et al.  Concentric-circle-based camera calibration , 2012 .

[3]  Yanxi Liu,et al.  Skewed Rotation Symmetry Group Detection , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Zhu Bin Determining Intrinsic and Pose Parameters of Camera Based on Concentric Circles , 2010, 2010 International Conference on Digital Manufacturing & Automation.

[5]  Jun-Sik Kim,et al.  A Camera Calibration Method using Concentric Circles for Vision Applications , 2001 .

[6]  Yasuyuki Matsushita,et al.  Camera calibration with lens distortion from low-rank textures , 2011, CVPR 2011.

[7]  Pablo A. Parrilo,et al.  Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..

[8]  V. Frémont,et al.  DIRECT CAMERA CALIBRATION USING TWO CONCENTRIC CIRCLES FROM A SINGLE VIEW , 2002 .

[9]  Xu Chen,et al.  A linear approach for determining camera intrinsic parameters using tangent circles , 2014, Multimedia Tools and Applications.

[10]  John Wright,et al.  RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Roland Siegwart,et al.  Vision Based Position Control for MAVs Using One Single Circular Landmark , 2011, J. Intell. Robotic Syst..

[12]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[13]  Hua Tang,et al.  Simple method for camera calibration of roundabout traffic scenes using a single circle , 2014 .

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

[15]  Hua Li,et al.  A new easy camera calibration technique based on circular points , 2003, Pattern Recognit..

[16]  ZhangZhengyou A Flexible New Technique for Camera Calibration , 2000 .

[17]  Yi Ma,et al.  TILT: Transform Invariant Low-Rank Textures , 2010, ACCV.

[18]  Andrea Torsello,et al.  Camera Calibration from Coplanar Circles , 2014, 2014 22nd International Conference on Pattern Recognition.

[19]  Zhengyou Zhang,et al.  Camera calibration with one-dimensional objects , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Hongbin Zha,et al.  Camera calibration from a circle and a coplanar point at infinity with applications to sports scenes analyses , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Yuan Li,et al.  Calibration of a robot hand-eye system with a concentric circles target , 2015, 2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

[22]  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..