Self-calibration of Camera with Rotary Motion Based on SIFT Feature Matching

Abstract

In the field of computer vision and photogrammetry, it is constantly necessary to online or real-time acquire the camera parameters through self-calibration. This paper presents a method about self-calibration of camera with rotary motion based on SIFT feature matching. The proposed approach first shoots more than three images of the same scene by rotating the camera and keeping its position and internal parameters unchanging. After SIFT feature extraction and sequentially cycled matching for all images, the optimal reference image and effective images are determined by virtue of the algorithm on pose estimation. According to the coordinates of matched SIFT features, all 2D projection transformation matrices which transforms the reference into other effective images are calculated. With these matrices, relevant linear equations are established and the internal matrix of camera is solved. The proposed method can be online applied to quickly, accurately and stably obtain internal parameters of camera. Real data has been used to test the proposed approach, and very good results have been achieved.