Internal corner detection of chessboard image for camera calibration based on 12 pixels symmetrical template

Dai Shijie; Shao Meng; Wu Jianing; Ge Shengqiang

The problem of chessboard image corner extraction always determined the three-dimensional measurement's accuracy of the camera calibration. By analyzing the defect for SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm that could not effectively distinguish the chessboard internal corners and edge points, the authors made use of the symmetry of the pixels around the internal corners, and proposed a symmetrical 12 pixels gray template detection algorithm. Firstly, a symmetrical 12 pixels USAN template was designed for fast distinguishing the internal corners and edge points. Meanwhile, both of the chessboard internal corners and smooth region would be treated as the candidates. Then the less gray variance of smooth region could be used to abandon them. At the same time, the proposed algorithm abandoned the external corners of the chessboard, which were very sensitive to the external factors, ensuring the precision of the corner extraction process. Experimental results show that the new method detects the nine order chessboard image by 1.244 577s, and its reprojection error was just [0.3, 0.3] pixels in Zhang's camera calibration. Both of these two indicators are better than the traditional SUSAN algorithm.

1. Tianjin Key Laboratory for Civil Aircraft Airworthiness and Maintenance,Civil Aviation University of China,Tianjin 300300,China;

2. Research Institute of Robotics and Automation,Hebei University of Technology,Tianjin 300130,China;

3. School of Astronautics,Harbin Institute of Technology,Harbin 150001,China

Received Date: 2013-08-10

Rev Recd Date: 2013-09-25

Publish Date: 2014-04-25

Key words:

chessboard image internal corners detection /
camera calibration /
symmetrical USAN template /
gray variance algorithm

Abstract: The problem of chessboard image corner extraction always determined the three-dimensional measurement's accuracy of the camera calibration. By analyzing the defect for SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm that could not effectively distinguish the chessboard internal corners and edge points, the authors made use of the symmetry of the pixels around the internal corners, and proposed a symmetrical 12 pixels gray template detection algorithm. Firstly, a symmetrical 12 pixels USAN template was designed for fast distinguishing the internal corners and edge points. Meanwhile, both of the chessboard internal corners and smooth region would be treated as the candidates. Then the less gray variance of smooth region could be used to abandon them. At the same time, the proposed algorithm abandoned the external corners of the chessboard, which were very sensitive to the external factors, ensuring the precision of the corner extraction process. Experimental results show that the new method detects the nine order chessboard image by 1.244 577s, and its reprojection error was just [0.3, 0.3] pixels in Zhang's camera calibration. Both of these two indicators are better than the traditional SUSAN algorithm.

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Internal corner detection of chessboard image for camera calibration based on 12 pixels symmetrical template

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