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790 JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 © 2011 ACADEMY PUBLISHER journals and international conferences. Her current research interests include: knowledge science and knowledge engineering, topic map. Dr. Lu is the member of ACM, IEEE, IEICE and CCF. Now she is also working in the computer science and technology post-doctoral research center of Jilin University. Liang Hu is a professor at the college of computer science and technology, Jilin University, Changchun, China. His current research interests include: knowledge science, computer network. He has published more than fifty research articles in referred journals and international conferences. Gang Liu is a professor at the college of software engineering, Changchun University of Technology, Changchun, China. His current research interests include: software engineering, knowledge science. He has published 5 research articles in referred journals and international conferences.
JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 791 An Automated X-corner Detection Algorithm (AXDA) Fuqing Zhao School of Computer and Communication,Lanzhou University of Technology,Lanzhou,Gansu,P.R.China Key Laboratory of Gansu Advanced Control for Industrial Processes Email: fzhao2000@hotmail.com Chunmiao Wei School of Computer and Communication,Lanzhou University of Technology,Lanzhou,Gansu,P.R.China Email: h09310917@126.com Jizhe Wang School of Computer and Communication,Lanzhou University of Technology,Lanzhou,Gansu,P.R.China Email: wangjizhe2009@mail2.lut.cn Jianxin Tang School of Computer and Communication,Lanzhou University of Technology,Lanzhou,Gansu,P.R.China Email: tangjianxin2009@mail2.lut.cn Abstract—According to the central symmetry and brightdark alteration of the four peripheral regions at the Xcorner, an automated X-corner detection algorithm (AXDA) is presented to camera calibration problem. By detecting the gray changes of the image, the algorithm can locate the position of X-corner accurately using the minimum correlation coefficient of the symmetry regions. Cross points of intersection are calculated using the detection points and the least square straight line fitting algorithm. The method can not only realize the sub-pixel X-corner extraction, but also resolve the low automation degree problem of the present detection algorithm under complex background. Experiment results show that the algorithm is an easilyrealized, highly-automated and robust method for rotation transform and brightness transform of the X-corner image. Index Terms—chessboard, corner detection, camera calibration I. INTRODUCTION Camera calibration[1,2] based on the chessboard planar template consists of corner detection and parameter calculation. Calibration is to create the constraint between the world coordinates and the image coordinates of the chessboard feature points. So X-corner detection is the essential technology for camera calibration. At present, X-corner detection algorithm can be classified as follows. One is the traditional corner detection algorithm, such as Harris operator [3,4]and SUSAN [5,6]. These algorithms are versatility and Manuscript received December 15, 2010; accepted January 17, 2011. This work is financially supported by the National Natural Science Foundation of China under Grant No.61064011 © 2011 ACADEMY PUBLISHER doi:10.4304/jsw.6.5.791-797 straightforward but are invalid for the vagueness of the image. The idea of Harris corner detection is simple and the corners extracted by this algorithm distribute uniformly. Besides, this algorithm is insensitive to the rotation of the image and the alteration of the image gray. However, in the X-corner image, the image blurring in the corner area usually causes a high Harris respond value of one or several points, which makes it difficult to determine the position of the corner exactly. SUSAN operator is proposed by Smith and Brady. The operator is a completely different corner detection method, which mainly based on light contrast. Do not have to calculate image difference. The calculation speed is so fast that the SUSAN are widely used in the higher requirements of real-time areas. The other is the curve fitting based algorithm. Algorithms based on the two times Radon transform [7] are the representative ones. But there will be a great error if image distortion exists. And there are some other detection algorithms [8,9] aimed at the characteristics of the chessboard image. But [8] is computational expensive. Although [9] is straightforward in computation, great error will generate after binarization processing. All the above algorithms are liable to be influenced by light and background image in practical applications and will inevitably result in missing error, detection error or false error, which leads the automation detection hard to realize. Although the calibration toolbox in Matlab[10] resolves these problems, the automation level for camera calibration decreases greatly since four points should be selected manually before the corner extraction. An automated X-corner detection algorithm (AXDA) is provided by studying the present methods carefully. This algorithm can detect the corner using the unique feature of X-corner so as to avoid detection error, false
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