Camera calibration is critical in computer vision measurement system, affecting the accuracy of the whole system. Many camera calibration methods have been proposed, but they cannot consider precision and operation co...Camera calibration is critical in computer vision measurement system, affecting the accuracy of the whole system. Many camera calibration methods have been proposed, but they cannot consider precision and operation complexity at the same time. In this paper, a new technique is proposed to calibrate camera. Firstly, the global calibration method is described in de-tail. It requires the camera to observe a checkerboard pattern shown at a few different orientations. The checkerboard corners are obtained by Harris algorithm. With direct linear transformation and non-linear optimal algorithm, the global calibration pa-rameters are obtained. Then, a sub-regional method is proposed. Those corners are divided into two groups, middle corners and edge corners, which are used to calibrate the corresponding area to get two sets of calibration parameters. Finally, some experimental images are used to test the proposed method. Experimental results demonstrate that the average projection error of sub-region method is decreased at least 16% compared with the global calibration method. The proposed technique is simple and accurate. It is suitable for the industrial computer vision measurement.展开更多
基金Tianjin Research Program of Application Foundation and Advanced Technology(No.14JCYBJC18600,No.14JCZDJC39700)the National Key Scientific Instrument and Equipment Development Project(No.2013YQ17053903)
文摘Camera calibration is critical in computer vision measurement system, affecting the accuracy of the whole system. Many camera calibration methods have been proposed, but they cannot consider precision and operation complexity at the same time. In this paper, a new technique is proposed to calibrate camera. Firstly, the global calibration method is described in de-tail. It requires the camera to observe a checkerboard pattern shown at a few different orientations. The checkerboard corners are obtained by Harris algorithm. With direct linear transformation and non-linear optimal algorithm, the global calibration pa-rameters are obtained. Then, a sub-regional method is proposed. Those corners are divided into two groups, middle corners and edge corners, which are used to calibrate the corresponding area to get two sets of calibration parameters. Finally, some experimental images are used to test the proposed method. Experimental results demonstrate that the average projection error of sub-region method is decreased at least 16% compared with the global calibration method. The proposed technique is simple and accurate. It is suitable for the industrial computer vision measurement.
基金Supported by National Natural Science Foundation of China （60575019）, the National High Technology Research and Development Program of China （863 Program） （2006AA01Zl16）, and Institute of Automation Chinese Academy of Sciences Innovation Fund For Young Scientists
文摘提出一种新的摄像机标定方法,该方法基于2D共面参照物摄像机标定方法和傅里叶条纹分析方法.将已知相位分布的平面二维正弦灰度调制条纹图作为平面标定靶,通过傅里叶条纹分析方法计算出两个截断正交相位分布,利用截断正交相位分布并结合二维正弦条纹图特点提取相应的图像特征点,建立像素坐标与2D平面坐标的对应关系.将该二维平面靶在摄像机成像空间中放置不同的位置,并完成相应的特征点提取,根据2D共面参照物摄像机标定方法即可完成摄像机标定.该方法利用平面相位测量的高准确度来提高标定特征点的提取准确度,从而提高标定准确度.实验对双摄像机系统进行了标定,标定后该系统对标定靶进行测量,标准偏差达到0 .010 mm.
