利用Gabor方向导数相关矩阵的角点检测

为了提高角点检测的准确率，提出了一种使用图像的Gabor方向导数构建相关矩阵来进行图像角点检测的算法。算法首先通过Canny边缘检测算法提取检测图像的边缘轮廓；然后使用Gabor滤波器对图像进行平滑，利用每一个边缘像素和其邻近像素的Gabor方向导数构建相关矩阵,若相关矩阵的归一化特征值的和大于预定阈值并且是局部极大值，则标记该像素为角点。算法利用邻近像素Gabor方向导数之间的相关信息提取角点，与传统的基于轮廓的角点检测算法相比，检测性能更加稳健。实验结果表明：在含噪声和无噪声情况下，提出的算法检测到的真实角点更多，而错误角点更少，整体性能有明显提升

Comer detection algorithm using correlation matrix of Gabor directional derivatives

To improve the accuracy of the corner detection, a new corner detection algorithm was proposed, and it used the Gabor directional derivatives of each pixel to construct the correlation matrix on edge contour.to detect corner. The algorithm firstly extracted the edge map of an image using the Canny edge detector; secondly, the image was smoothed by the Gabor filters and the correlation matrixes were constructed using Gabor directional derivatives of each edge pixel and its surrounding pixels. If the sum of the normalized eigenvalues was not only above the previously specified threshold but also the local maximum, it would be labeled as a corner. Compared with the traditional contour-based corner detection algorithm, it used the related information of the Gabor directional derivatives of the edge pixel and its surrounding pixels to extract corners, hence achieving better robustness to noise. The experimental results indicate that: The proposed algorithm detects more matched corners and fewer false corners in the noise free and noisy cases and achieves obvious improvement in performance.