A Robust Chessboard Detector for Geometric Camera Calibration

We introduce an algorithm that detects chessboard patterns in images precisely and robustly for application in camera calibration. Because of the low requirements on the calibration images, our solution is particularly suited for endoscopic camera calibration. It successfully copes with strong lens distortions, partially occluded patterns, image blur, and image noise. Our detector initially uses a sparse sampling method to find some connected squares of the chessboard pattern in the image. A pattern-growing strategy iteratively locates adjacent chessboard corners with a region-based corner detector. The corner detector examines entire image regions with the help of the integral image to handle poor image quality. We show that it outperforms recent solutions in terms of detection rates and performs at least equally well in terms of accuracy.