An improved checkerboard detection algorithm based on adaptive filters

Checkerboard corner extraction is a crucial step in camera calibration. However, most existing algorithms are not good enough if the lens distortion is too large. This study aims to propose a checkerboard corner detection algorithm based on adaptive filters to address this problem. First, adaptive filters based on local image features are used to generate response maps of checkerboard images. Next, a non-max suppression and a scoring system are applied for further screening. Finally, the whole checkerboard structure is restored via inertia growth. The algorithm proposed is subject to rigorous experimental validation using synthetic and real images. Compared with several state of the art methods, our algorithm has the best performance.