Structural-information-based robust corner point extraction for camera calibration under lens distortions and compression artifacts

Abstract

Previous camera calibration methods often use a checkerboard to capture images and estimate the camera parameters from the correspondences between images and the checkerboard. The corner points in the checkerboard images are used as useful features for correspondence matching. Therefore, it is essential to precisely find the corner points in the checkerboard images. In many previous works, the corner points are extracted assuming that the checkerboard images are not distorted by its lens. Instead, image blurring and Gaussian noise on the images are usually considered, but other cases are not dealt with. However, the captured checkerboard images are often corrupted by lens distortions and compression artifacts, which leads to performance degradation of corner point extraction. Moreover, the corner points are extracted individually in the previous methods without considering their geometric relations. To better handle the corner point extraction problem under lens distortions, in our corner point extraction optimization, the distorted locations of the pixels on checkerboard images are corrected with the camera parameters, and the structural constraints for checkerboard image grids are then applied under the line-to-line mapping. Also, to robustly find the blurred edges between corner points due to JPEG compression, an edge surface model is newly proposed that models the transitions with over- and under-shoots around the blurred edges. Extensive experimental results show the effectiveness of our method that significantly outperforms existing methods in finding the corner points, thus leading to robust camera parameter estimations under compression and lens distortions for synthetic and real data. In addition to this, our study proposes a method to find checkerboard images suitable for the camera calibration based on RANSAC. We suggest new patterns for the camera calibration and evaluate the patterns theoretically and experimentally by the accuracy of the extracted corner points.