一种基于交叉熵的黑白棋盘角点检测算法

摘要

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.%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。