期刊文献+

采用分离式差分标定靶的单摄像机标定方法 被引量:26

Calibration Method with Separation Pattern of a Single-Camera Based on Difference Coordinates
原文传递
导出
摘要 采用分离式差分标定方法实现对单摄像机的高精度标定。在摄像机的物面位置上放置多个小尺度的分离式标定靶,每个标定靶上有标准点阵列,对每个光点由高斯曲面拟合法得到其CCD像面上的位置,光点位置检测结果的稳定性(均方根)达到1μm(相当于CCD 0.0053 pixel)。利用同一标定靶上标准点之间的物面差分坐标,进行四次曲面拟合,建立摄像机的像面与物面之间的映射关系。该方法对各个标定靶之间的距离没有提出要求,采用多个小尺寸的差分标定靶代替整体式的大尺度标定靶,降低对大尺度标定靶的制作要求。实验表明,当各个标定靶之间的相对角度误差小于60″时,标定残差平均值可以达到4.8μm(相当于CCD 0.028 pixel)。 The difference calibration and separation patterns are used to calibrate a single-camera with high precision. Several separation calibration patterns with small size are put on the object plane of the camera. Each pattern has some spot array with high precision. Gaussian curved-surface fitting method is used to get the position of spot on the CCD image plane, and the stability (RMS) of measurement result of the spot position is 1μm (about 0. 0053 CCD pixel). The object plane difference coordinates of the reference points on the same calibration pattern are used to calibrate the camera. The mapping relationship between the object plane and the image plane is established with the biquadratic curved-surface fitting method. There is not need for the distance between the patterns. The integer pattern with large dimension is replaced by the several small difference patterns. The difficulty to manufacture the large pattern is avoided. When the error of the relative angle between the patterns is less than 60". The experiment results show that the calibrated mean value of residual error is 4.8μm (about 0. 028 CCD Dixels).
出处 《光学学报》 EI CAS CSCD 北大核心 2006年第5期697-701,共5页 Acta Optica Sinica
关键词 机器视觉 摄像机标定 差分式标定 分离式标靶 曲面拟合 machine vision camera calibration difference calibration separation pattern curved-surface fitting
  • 相关文献

参考文献12

二级参考文献19

  • 1Jolly M P D, Lakshmanan S, Jain A K. Vehicle segmentation and classification using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(3) :293-308.
  • 2Lakshmanan S, Grimmer D. A deformable template approach to detecting straight edges in Radar images. IEEE Trans. On Pattern Analysis and Machine Intelligence, 1996, 18(4) .-438-443.
  • 3Yuille A L, Hallinan P W. Feature extraction from face susing deformable template. International J. Computer Vision, 1992, 8(2) :99-111.
  • 4Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6) :679-698.
  • 5Williams D J, Shan M. A fast algorithm for active contours and curvature estimation. CVGIP : Image Understanding, 1992, 55(1) :14-26.
  • 6Lam K M, Yan H. Fast greedy algorithm for active contours. Electron. Lett. , 1994, 30(1):21-23.
  • 7侯成刚,杨文献,屈梁生.一种快速检测圆心的抗噪声亚像素算法[J].光学学报,1998,18(4):481-485. 被引量:25
  • 8杨长江,汪威,胡占义.一种基于主动视觉的摄像机内参数自定标方法[J].计算机学报,1998,21(5):428-435. 被引量:38
  • 9成罡,金国藩,邬敏贤,何庆生,刘海松,严瑛白.利用不确定性提高击中击不中变换的抗畸变能力[J].光学学报,1999,19(2):155-162. 被引量:1
  • 10李华,吴福朝,胡占义.一种新的线性摄像机自标定方法[J].计算机学报,2000,23(11):1121-1129. 被引量:46

共引文献327

同被引文献241

引证文献26

二级引证文献260

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部