关键词: 摄像机自标定, Kruppa方程, 尺度不变特征变换（SIFT）, 张量投票, 基础矩阵

Abstract: In order to improve robustness of the traditional camera self-calibration algorithm based on Kruppa equations, the new method of camera self-calibration based on tensor voting is first proposed. The SIFT algorithm, based on scale invariant property, is adopted to extract and match the feature points of each image, which are taken from three different angles to the same scene. The first eight feature points with robustness are figured out with tensor voting and sorting. Then the fundamental matrix and the pole points are calculated by 8 points’ algorithm, and finally the parameter matrices of camera can be obtained by the Kruppa equations and the Structure From Motion（SFM） algorithm. Compared with other algorithms through the comparative experiments, this method is proven to be more accurate, meanwhile, it can be regarded as a new robust camera self-calibration algorithm by the simulation experiments with Gaussian noise.

Key words: camera self-calibration, Kruppa equations, Scale Invariant Feature Transform（SIFT）, tensor voting, fundamental matrix