Abstract: A new corner detection algorithm based on the autocorrelation matrix of Multi-channel Odd Gabor grAdient (MOGA) was proposed to suppress the decrease of corner positioning accuracy caused by the smoothed edge. The input image was transformed by 8-channel odd Gabor filter, and then autocorrelation matrices were constructed for each pixel by Gabor gradient correlation of the pixel and its surrounding pixels. If the sum of the normalized eigenvalues of the pixel was local maxima, the pixel was labeled as a corner. Compared with the classical algorithms, such as Harris and Curvature Scale Space (CSS), the proposed algorithm increased the average rate of correct detection by 17.74%, and decreased the average rate of positioning error by 18.15%. The experimental results show that the proposed algorithm has very good detection performance, and gets higher corner detection rate and better corner positioning accuracy.

Key words: edge smoothing, positioning accuracy, corner detection, Harris algorithm, Curvature Scale Space (CSS) algorithm

摘要： 为了抑制边缘轮廓平滑导致角点定位精度的下降，提出多通道奇Gabor梯度相关矩阵的角点检测算法。该算法是在Gabor滤波器的基础上，利用8个通道的奇Gabor滤波器对输入图像进行变换；然后利用每个像素与其相邻像素的Gabor梯度相关性构造自相关矩阵，若像素点的自相关矩阵对应的归一化特征值的和是局部极大值，则标记为角点。实验显示，与Harris算法、曲率尺度空间(CSS)算法等经典算法相比，该算法的平均正确检测率提高了约17.74%，平均定位误差降低了约18.15%。结果表明，所提出的算法具有更好的检测性能，并获得了较高的角点检测率及较好的定位精度。

关键词: 边缘轮廓平滑, 定位精度, 角点检测, 哈里斯算法, 曲率尺度空间算法