The main functions of machine vision can be divided into two categories: positioning and recognition. Recognition mainly refers to obtaining image information from the camera and calculating geometric information of objects in three-dimensional space to reconstruct and recognize objects.
The relationship between the 3D geometric position of a point on the surface of a space object and its corresponding point in the image is determined by the camera imaging geometric model, and these geometric model parameters are camera parameters. Under most conditions, these parameters can only be obtained through experiments and calculations. This process is called camera calibration (or calibration).
The camera calibration process is to determine the geometric and optical parameters of the camera, as well as the orientation of the camera relative to the world coordinate system. Because of the size of the calibration accuracy, it directly affects the accuracy of the machine vision system. Therefore, only after the camera calibration is completed, can the follow-up work be carried out normally. It can be said that improving the calibration accuracy is also one of the important aspects of the current scientific research work.
When calibrating the camera, it is necessary to establish the geometric model of the camera imaging. The geometric model of the camera can be obtained through the camera shooting a flat plate with a fixed spacing pattern array and the calculation of the calibration algorithm, so as to obtain high-precision measurement and reconstruction results. The flat plate with a pattern array with fixed spacing is a calibration plate, also known as a calibration template. Therefore, it can be said that the use of the calibration plate is an indispensable assistant in the camera calibration process.
