Detection of sub-pixel chessboard corners based on gray symmetry factor

Abstract

In order to solve the robustness and efficiency problems of chessboard corner detection under on-site condition, a method based on the square-closed loop template and local gray symmetry factor is proposed for detecting sub-pixel chessboard corners with high precision and efficiency. Interest points on edges of original image is detected by square-closed loop template according to the transition times on the template, on this basis, corners are roughly detected by averaging the adjacent coordinates of interest points; gray symmetry factors, calculated in the local neighborhood of roughly detected corners, are used as the weighting factors to precisely detect corners on sub-pixel level. Experimental results indicate that computing speed and positioning accuracy of this method was obviously increased. The corner detection performance could be significantly improved using the proposed method.