目的 传统的基于边缘轮廓的角点检测算法需要计算每一个边缘像素点的曲率，对噪声和局部变化敏感，极易造成检测结果的不稳定。针对这一问题，提出一种利用点弦距离递归的角点检测算法。方法 首先，利用Canny边缘检测器提取边缘轮廓线。其次，用3个不同尺度的高斯核对边缘线进行平滑，对每一个高斯尺度平滑后的边缘线，连接首尾端点形成一条弦，计算边缘轮廓上每个边缘像素点到弦的距离，将点弦距离最长的像素点标为候选角点，该像素点将原边缘轮廓线分成两条边缘，然后将该像素点与首尾端点连接成两条弦，重新计算点弦距离，将所有距离大于设定阈值的点作为候选角点。最后，利用多尺度技术对候选角点进行判决并得到最终角点。结果 与现有的基于曲率计算的角点检测算法相比，本文算法不需要计算一、二阶导数，有效避免了局部变化带来的计算误差。通过计算得到4个角点检测器的平均排名依次为Harris （4.0）、He&Yung （2.67）、CPDA （1.83）、本文算法 （1.5）。与其他3种经典的角点检测算法相比，本文提出的检测算法排名第一，因此表现出了更好的检测性能。结论 提出了一种新的利用点弦距离递归的角点检测算法。从实验结果看，本文提出的角点检测器在图像仿射变换、JPEG质量压缩和高斯噪声条件下有更好的平均重复性和定位误差。

Objective Corners in images represent critical information in describing object features, which play a crucial and irreplaceable role in computer vision and image processing systems. Many computer vision tasks rely on the successful detection of corners, including 3D reconstruction, stereo matching, image registration, motion estimation, and object tracking. However, no strict mathematical definition for corner exists; corners are usually defined as points with low self-similarity or locations where variations in intensity in all directions are high. Alternatively, corners may be defined as image points containing the local maxima of curvature on the edge contour or the intersection of two of more edge curves. Many promising corner detection methods based on different corner definitions have been proposed by vision researchers. However, the traditional contour-based corner detection algorithm needs to calculate the curvature of each edge pixel and is sensitive to noise and local variations, thereby causing the instability of detection results. Therefore, this study proposes a novel image corner detection approach based on a recursive point-to-chord distance. Method This study analyzes state-of-the-art corner detection algorithms then proposes a new corner detection method. First, it extracts each edge contour from the input image using the Canny edge detector, which is one of the most widely used edge detectors in contour-based corner detection and has become a standard gauge in edge detection. An edge pixel appears when the gradient magnitudes at either side of it are lower than itself. However, the output contours may have small gaps, and these gaps may possibly contain corners. Second, it smooth curves by using three different Gaussian kernels. For each smoothed curve of Gaussian scale, the ends of the curve are connected, forming a chord. Then, the distance between each edge pixel of the contour and the chord is calculated, and the pixel with the longest distance is marked as the candidate corner. The original edge contour is divided into two edges by using the pixel point. Then, the pixel point is connected to the ends of the contour into two chords. The distance from the point to the chord is recalculated and compared with the threshold value. We select the point that is greater than the threshold as the candidate corner. Finally, the multi-scale technique is applied to the candidate corner set, and the final corners are obtained. Result Compared with existing corner detection algorithms based on curvature calculation, the proposed algorithm does not need to calculate the first and second derivatives, effectively avoids the calculation error caused by local variation effectively, and is highly robust to noise. The four corner detectors achieve the highest average repeatability in JPEG quality compression and the worst localization error in shear transformation. The proposed and CPDA corner detectors perform better than the other detectors in geometric transformations. In terms of JPEG quality compression and Gaussian noise, the proposed method achieves the highest average repeatability and lowest localization error than the three other detectors. Experimental results show that the proposed detector attains better overall performance. Conclusion The proposed detector does not need to accumulate each distance from a moving chord nor does it need to compute the accumulation of each point on a curve, thereby achieving good speed while keeping good average repeatability and accuracy. Compared with the three classic detection algorithms of Harris, CPDA, and He and Yung, the proposed detector attains better performance in average repeatability and localization error under affine transforms, JPEG compression, and Gaussian noise. Existing corner detection methods can be broadly classified into three classes: intensity-, model-, and contour-based methods. The aim of intensity-based corner detection is to extract local gray variation and structural information effectively. Model-based methods extract corners by fitting the local image into a predefined model. Contour-based methods obtain the image’s planar curves by using an edge detector, smooth the curves by using a Gaussian function, and compute the corresponding curvatures. Finally, the points of local curvature maxima, line intersects, or rapid changes in edge direction are marked as corners. The two categories of methods have strengths and weaknesses, and their defects in practical application have been revealed, making corner detection a research hotspot in computer vision and image processing. Experiment results show that the proposed corner detector performs better than the other three classical detectors in terms of robustness. The corner detection algorithm in this study has good detection performance. Future tasks may continuously improve the algorithm’s detection performance and apply it to many computer vision studies.