目的 角点是图像的基本特征，在图像处理与计算机视觉系统中，经常作为复杂计算的第1步，例如，目标识别、目标跟踪等。因此，角点检测器的检测性能显得尤为重要。基于此，提出了一个既利用到图像边缘轮廓信息又利用到图像灰度信息的基于Log-Gabor梯度方向一致性的角点检测算法，以提高角点检测器的检测性能。方法 根据角点的定义可知，角点在各个方向的灰度变化都很大，并且每个角点的梯度方向与相邻像素的梯度方向都具有很大差别。然而，相邻边缘像素点的梯度方向是一致的，都是垂直于边缘脊的方向。因此，本文利用角点与边缘像素的这一特性，构建了一个新的角点测度。该算法首先利用边缘检测器检测并提取图像的边缘映射；然后利用Log-Gabor虚部滤波器提取边缘像素周围的灰度变化信息，找到边缘像素点的梯度方向，利用梯度方向计算新的角点测度；最后对角点测度进行阈值化处理，得到最终的角点检测结果。结果 提出的算法分别与CPDA（chord-to-point distance accumulation）算法，He & Yung算法，以及Harris算法在标准轮廓图像和仿射变换下进行性能比较。平均重复率与定位误差分别作为评价角点检测器检测稳定性以及定位性能的指标。从平面曲线上的仿真实验结果可以看到，本文提出的角点检测算法能够较好地检测到真实角点，避免对角点的漏检与误检。旋转变换、非统一尺度变换以及高斯噪声下的平均重复率和定位误差结果的平均排名CPDA为2.00， Harris为3.33，He & Yung为2.83，本文算法为1.67。实验结果表明，本文算法的综合性能最优。本文算法优于其他3种角点检测算法，包括检测稳定性能和定位性能。结论 基于边缘的角点检测算法大多只依赖于图像的边缘轮廓信息，没有考虑到图像的灰度变化，而基于灰度的角点检测算法大多只考虑到图像的灰度信息。本文算法既考虑到图像的边缘形状也考虑到图像的灰度变化，并且利用log-Gabor虚部滤波器充分的提取图像的局部信息。在此基础上，利用图像边缘像素的梯度方向一致性构建了新的角点测度，以提高角点检测器的检测性能。实验结果表明，本文算法拥有良好的角点检测稳定性与定位性能。

Objective Many local features, such as corner and inflection points, exist in images. The corner is the basic feature of an image, and it is always defined as a point where at least two edges intersect, a point having the maxima curvature, or a point around which a significant change in intensity occurs in all directions. Many corner detectors are available, and existing approaches can be broadly classified into edge-based, model-based, and gray-based methods. Corner detectors have their respective advantages and disadvantages. Model-based corner detectors detect corner points by matching image patches to the predefined templates. However, predefined templates cannot easily cover the full corner in natural images. Gray-based corner detection algorithms measure the local intensity variation of an image to find corners. These methods are sensitive to local variation and not robust to noise. Edge-based methods extract the edges from the input image and analyze the edge's shape to find corners. These approaches cannot fully extract image information. Corner detection is a challenging task in image processing and computer vision systems, such as object recognition, object tracking, simultaneous localization and mapping (SLAM), and pattern matching. Therefore, the performance of the corner detector is important. To improve the detection performance of the corner detector, this paper presents a new corner detector that combines the edge contour and gray information of the image and utilizes the consistency of the edge pixels with the log-Gabor gradient direction.Method According to the definition of “corner,” we know that intensities around a corner change extremely in every direction. The gradient direction of a corner with adjacent pixels differs significantly. However, the gradient direction of adjacent edge pixels is the same and perpendicular to the ridge of the edge. This study uses this characteristic to construct a new corner measure. The proposed algorithm employs the Canny edge detector to detect and extract the edge map of an input image. Then, the imaginary parts of log-Gabor filters are used to smooth the edge pixels along multi-directions, and the corresponding gradient directions of pixels are determined. The obtained gradient directions are used to construct the new corner measure. Afterward, both the corner measure and angle threshold are used to remove false and weak corners.Result The proposed detector is compared with three corner detectors on planar curves and under affine transforms. We also evaluate the performance under Gaussian noise degradation. To evaluate the performance of four detectors on planar curves, two published test image shapes of different sizes are selected. Ten different common images from standard databases are also used to evaluate the performance under affine transforms and Gaussian noise degradation. Average repeatability and localization error are the two evaluation criteria. Average repeatability measures the average number of repeated corner points between affine-transformed and original images. The localization error measures the localization deviation of the repeated corner. In the simulation experiments, the average rankings of the four approaches are as follows:CPDA is 2.00, Harris is 3.33, He and Yung is 2.83, and proposed method is 1.67. Experimental results show that the proposed method presents excellent performance in terms of average repeatability and localization error under affine transform and Gaussian noise degradation. The number of false and missed corners in published test images is less than that of the three other corner detectors in the experiments. High computation complexity is the shortcoming of the proposed method.Conclusion The edge-based corner detection algorithm mostly depends only on the edge shape of the image without considering the change in image gray. The gray-based corner detection algorithm only considers the gray information of the image. The proposed method considers the image edge shape and the gray changes. The imaginary parts of log-Gabor filters are used to smooth the edge pixels along multi-directions. Meanwhile, the consistency of the gradient directions of the edge pixels is used to construct the corner measure. Experimental results show that the proposed algorithm has good stability and corner detection performance. To address the high computation complexity of the proposed method, hardware measures, such as an embedded processor and FPGA controller, should be used to improve the problem of real-time processing. In the future, the optimized algorithm should be considered. Meanwhile, the proposed method can be applied to image matching.