视觉标定和校正是应用机器视觉的重要环节，可使检测的图像更加准确以达到提高机器视觉检测精度的目的，是不可忽视的关键步骤。提出了一种改进的平面模板法，在传统方法和Heikkila 模型基础上推广了切向畸变模型，成像模型中增加了高阶非线性畸变并进行了一定的简化，经过该模型优化标定参数并得到特征像素的空间校正模值，然后通过线性插值方法对像素进行灰度校正，得到标定结果和校正后的图像。该方法由自行搭建的机器视觉检测平台通过LabVIEW 工具实现，实验证明视觉系统采集的图像得到了良好的校正，校正精度达到0.17%。

Calibration and rectification for the computer vision sensor are the key links of the application of machine vision system which will make the image more accurate so as to impove detection precision of the machine vision system. It is a key step that can not be ignored. An improved planar template method is presented, which extends the tangential distortion model based on the traditional method and the Heikkila model. The imaging model increases the high order nonlinear distortion and has some simplification.Through the model calibration parameters optimized and characteristics of the pixel space rectification value are obtained. The pixel grayscale is corrected by the linear interpolation method, the calibration results and the corrected image can be obtained. The method is implemented through LabVIEW tools in the machine vision detection platform built by the authors. Experiment verifies that images collected by the visual system are corrected well and the rectification accuracy reaches 0.17%.