A New Chessboard Corner Detection Algorithm with Simple Thresholding.

Chessboard detection and corner extraction are imperative during the camera calibration process, which is a fundamental work in computer vision. This paper describes a new chessboard corner detection algorithm using the amplitude spectrum feature of circular sampling at each point as corner response function. A simple thresholding technique is that only the points near a chessboard corner will get positive corner response. However, image noise will bring a lot of false positives. The distribution of false positive response with respect to image noise is given, this distribution also has a property like the three-sigma rule in Gaussian distribution, that can be used as thresholding rule. The only parameter needed is the standard deviation of image noise, which can be measured using some patches of the image for once. Experiment is presented showing the efficiency of the proposed method against noise, compared with existing algorithm under simulated image.