一种基于交叉熵的黑白棋盘角点检测算法

Chessboard Corner Detection Algorithm Based on Minimum Cross Entropy

分析了现有黑白棋盘角点检测算法存在的不足,将交叉熵思想引入角点检测中。该算法首先将角点周围像素划分为4个象限,通过相邻象限间的像素灰度差实现角点初选;其次,给出对角象限灰度交叉熵定义,根据局部交叉熵最小原理实现角点筛选;第3,针对备选角点局部重叠的问题,采用梯度幅值非极大值抑制方法实现像素级角点定位;最后采用Frostner算子实现角点的亚像素坐标解算。实验结果显示该算法检测结果优于经典Harris算子以及SV算子,获取的角点亚像素坐标精度与Matlab相机标定工具箱相当,同时易于实现在线标定。

The shortcoming of the present B/W chessboard corner detection algorithm is analyzed and a new method based on cross entropy is proposed. Firstly,the pixels around the corner are divided into 4 quadrants,and initial selection of corners is carried out based on the gray value difference between the adjacent quadrants; secondly,the cross entropy of the diagonal quadrant is defined,and the corner screening is done using the principle of minimum cross entropy; thirdly,the idea of non-maximum suppression of local gradient amplitude is introduced to solve the problem of local overlap of the candidates; at last,sub-pixel coordinates of corners are calculated using Frostner Operator. Experiments and their analysis prove preliminarily that：（1） the detection result of this algorithm is better than the classical Harris Operator and SV Operator;（2） the sub-pixel accuracy obtained is almost the same as that obtained with the Matlab Camera Calibration Toolbox,and it is suitable for online camera calibration.