主要讨论两方面的工作.首先,对三视校正问题进行理论分析,给出了校正后图像的基本矩阵与其约束条件之间的关系,讨论了三视校正过程中的6个自由参数的几何含义.这些结果为处理校正过程中带来的图像射影畸变提供了理论根据.其次,在RANSAC(random sampling consensus)框架下,提出了一种鲁棒的三视校正算法.与传统的校正算法不同,该算法不再只依赖于基本矩阵,而是直接利用了原始匹配点的信息.这种基于点的方法有两个优点:一方面,由于噪声的干扰,基本矩阵往往估计得不够准确;另一方面,由于基本矩阵的评价准则与校正结果的评价准则不同,即使从好的基本矩阵出发,也未必能获得好的校正结果.大量的模拟和真实图像实验表明,该算法具有很强的抗噪声及抗错误匹配的能力,能够获得令人满意的校正效果.“,”The main contributions are two-fold: Firstly, some theoretical analyses are carried out on trinocular rectification, including the relationship among the three rectified images and their three fundamental matrices, and an geometric interpretation of the 6 free parameters involved in the rectification process. Such results could be used as a theoretical guide to reduce the induced projective distortion. Secondly, under the RANSAC (random sampling consensus) paradigm, a robust trinocular rectification algorithm is proposed. Unlike the traditional ones where only the fundamental matrices are used to rectify images, this algorithm instead uses directly corresponding points for the rectification. The main advantage of this point-based approach is that on one hand, the computation of fundamental matrices is usually prone to noise; on the other hand, good fundamental matrices do not necessarily always produce good rectified images because the two processes have different evaluation criteria. Extensive simulation and experiments with real images show that the proposed rectification technique is resistant to noise as well as to outliers of the corresponding points, and fairly good rectification results can be obtained.