Fully automatic matching of circular markers for camera calibration

Camera calibration is one of critical steps in computer vision, also an exhaustive process because substantial human computer interactions are frequently required to deal with the matching problem. In this paper, an automatic matching method of markers for camera calibration is presented based on the local architecture characteristics of a new planar circle pattern, which can be applied to a wide range of spoiled images including those of distortion, noise, and blur. Firstly, a robust ellipse detector is developed to extract ellipses from the image. After the centroids of the ellipses are triangulated into triangle network by Delaunay method to find k annular neighborhood of each ellipse, a cost function is defined to locate orientation ellipses for the homography matrix between the image and the calibration pattern. Then the correspondences between the marker ellipses and the marker circles are established by a homography method followed by a point sets registration strategy. Finally, the method is tested with real and transformed images to show its accuracy and robustness, which is entirely unsupervised and easily embedded into the exiting algorithms.