Automated checkerboard detection and indexing using circular boundaries

• Low probability of missing true corners due to user-defined parameters of feature extraction.
• Discarding outliers using characteristics of checkerboard corners.
• Index extension using characteristics of neighboring checkerboard corners.
• Performance of the proposed method in terms of success ratio.
• Robustness of the proposed method against partial view, lens distortion and image noise.

This paper presents a new algorithm for automated checkerboard detection and indexing. Automated checkerboard detection is essential for reducing user inputs in any camera calibration process. We adopt an iterative refinement algorithm to extract corner candidates. In order to utilize the characteristics of checkerboard corners, we extract a circular boundary from each candidate and find its sign-changing indices. We initialize an arbitrary point and its neighboring two points as seeds and assign world coordinates to the other points. The largest set of world-coordinate-assigned points is selected as the detected checkerboard. The performance of the proposed algorithm is evaluated using images with various sizes and particular conditions.