8 October 2015 Camera self-calibration method based on two vanishing points
Author Affiliations +
Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96752M (2015) https://doi.org/10.1117/12.2202761
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
Camera calibration is one of the indispensable processes to obtain 3D depth information from 2D images in the field of computer vision. Camera self-calibration is more convenient and flexible, especially in the application of large depth of fields, wide fields of view, and scene conversion, as well as other occasions like zooms. In this paper, a self-calibration method based on two vanishing points is proposed, the geometric characteristic of disappear points formed by two groups of orthogonal parallel lines is applied to camera self-calibration. By using the vectors’ orthogonal properties of connection optical centers and the vanishing points, the constraint equations on the camera intrinsic parameters are established. By this method, four internal parameters of the camera can be solved though only four images taken from different viewpoints in a scene. Compared with the two other self-calibration methods with absolute quadric and calibration plate, the method based on two vanishing points does not require calibration objects, camera movement, the information on the size and location of parallel lines, without strict experimental equipment, and having convenient calibration process and simple algorithm. Compared with the experimental results of the method based on calibration plate, self-calibration method by using machine vision software Halcon, the practicability and effectiveness of the proposed method in this paper is verified.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shaoli Duan, Huaping Zang, Mengmeng Xu, Xiaofang Zhang, Qiaoxia Gong, Yongzhi Tian, Erjun Liang, Xiaomin Liu, "Camera self-calibration method based on two vanishing points", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96752M (8 October 2015); doi: 10.1117/12.2202761; https://doi.org/10.1117/12.2202761


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