在计算机视觉中，如何由非定标序列图象得到相机和景物的度量特性是相机自定标技术要解决的主要问题.一般相机定标方法则是利用景物的已知三维点坐标或几何特性 (如正交方向的灭点等 )来确定相机的定标矩阵K,而相机自定标方法则是直接根据图象和相机内外参数的约束来确定相机和景物的度量特性.相机定标对于计算机视觉的许多应用来说是非常重要的，而在实际应用中，由于定标过程往往是很复杂的，因而，在过去的几年中，许多学者在相机自定标方面做了大量的工作，其研究结果表明，对于刚性景物来说，相机自定标是可行的.为了使摄像机自定标更鲁棒、速度更快，给出了一种新的基于线性模型的相机自定标方法，该方法是首先利用三点透视投影图、灭点和向量正交的性质来得到一组非线性方程，然后将其转换为线性方程组，以避免求解过程中的累积误差，最后高精度地求出了全部内参数 αx,αy,u0，v0.实验结果表明，此方法是有效的.

Self-calibration is the computation of metric properties of the cameras and the scene from a set of uncalibrated images. This is different from conventional calibration where the camera calibration matrixKis determined from the image of a known calibration grid or properties of the scene, such as vanishing point of orthogonal directions. Instead, in self-calibration the metric properties are determined directly from constraints on the internal or external parameters. Camera calibration is essential to many computer vision applications. In practice this often requires complicated calibration procedures to be carried out regularly. In the last few years a lot of work has been done on self-calibration. It has been shown that a metric calibration was possible based on the rigidity of the scene. Based on the pin-hole camera model,a new self-calibration method is presented in this paper. We can use the char- acteristic of 3-point perspective projective, vanish points and the orthogonal vector to get a set of equations and then all the intrinsic parametersαx,αy,u0,v0can be solved with high accuracy. The feasibility of the approach is illustrated on synthetic images.