Abstract: Corner point detection is an important part of camera calibration,as its detection accuracy will directly affect the accuracy of camera calibration.Camera calibration can determine the internal and external parameters of the camera by performing corner point detection on checkerboard images,but traditional methods preserve more redundant information while detecting the corners of the board,and fail to give accurate position of the corner points of the board.To address the problem,this paper proposes a more efficient checkerboard corner point detection method.The checkerboard corner point is characterized by the fact that it is the junction point of two pairs of symmetric local gray areas,and this feature can be used to build the matching template.The response point with higher matching degree is used as the candidate corner point.The threshold method is used to process the non-corner points,and the non-maximum value suppression and gradient statistics are used to screen the target corner point.Experimental results show that compared with the corner point detection methods which optimize the corner point set by checkerboard texture and geometric features,the proposed method can improve the detection accuracy,shorten the process of detection,and effectively detect the checkboard images and distorted images in complex environments.

Key words: template matching, corner point detection, sub-pixel positioning, non-maximum value suppression, gradient direction

摘要： 角点检测是摄像机校准的重要环节，可通过对棋盘图像进行角点检测来确定摄像机的内外参数，然而传统方法在检测棋盘角点时保留了较多的冗余信息，难以精确给出棋盘角点位置。为此，提出一种新的棋盘角点检测方法。根据棋盘角点位于两对对称局部灰度区域交界点的特点构建匹配模板进行匹配，将匹配度较高的响应点作为候选角点。同时采用阈值方法处理非角点，通过非极大值抑制和梯度统计法筛选出目标角点。实验结果表明，相比利用棋盘纹理和几何特征优化角点集的角点检测方法，该方法提高了检测正确率，缩短了检测时间，并能够有效检测出复杂环境下的棋盘格图像和畸变图形。

关键词: 模板匹配, 角点检测, 亚像素定位, 非极大值抑制, 梯度方向

CLC Number: 

• TP391.41