The experimental results demonstrate that the proposed method has advantages over the popular OpenCV chessboard corner detection method in terms of detection accuracy and computational efficiency. The proposed algorithm only requires a user input of the chessboard size, while all the other parameters can be adaptively calculated with a statistical approach. Finally, the sub-pixel location of each corner is calculated. After that, a simple yet effective approach is adopted to sort the detected corners into a meaningful order. Then, a novel strategy that utilises both intensity and geometry characteristics of the chessboard pattern is presented to eliminate fake corners from the initial corner set. In authors' method, an initial corner set is first obtained with an improved Hessian corner detector. In this study, an automatic chessboard corner detection algorithm is presented for camera calibration. Chessboard corner detection is a necessary procedure of the popular chessboard pattern-based camera calibration technique, in which the inner corners on a two-dimensional chessboard are employed as calibration markers.
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