Corner Localization in Chessboards for Camera Calibration

Camera calibration is a central topic in computer vision, since it is the first and fundamental step for image rectification, 3D modelling and reconstruction. Good results can be obtained using very well known camera calibration algorithms like the ones presented by Zhang or Tsai; both of them need an accurate initialization procedure that requires to determine the corner positions of a calibration pattern (e.g. a chessboard) with very high precision. In this paper we propose an efficient algorithm which determines the chessboard corners with subpixel precision; moreover it does not make any assumption on the scale and orientation of the chessboard, and works under very different illumination conditions. The method first localizes the chessboard in the image, then it determines the size of its squared elements, and finally it looks for the corners by means of a simple statistical model. The results presented show the accuracy and the robustness of the method.