目的 传统的曲率尺度空间角点检测中，选择的尺度不同会造成角点的漏检测及误检测问题。针对这两个问题，提出一种曲率尺度空间与链码方向统计的角点检测方法。方法 提出的角点检测方法是以曲率尺度空间为基础，先在较低的曲率尺度空间上选择候选角点集，再通过自适应阈值及链码方向统计的方法在角点集中剔除错误角点。结果 针对不同类型的图像进行了实验，结果表明，该方法比现有角点检测方法检测准确度高5%10%，且漏检测角点少，角点检测错误率低。实验将该方法与CSS算法进行计算时间对比，分别采用256×256简单场景图与935×715复杂场景图，该方法计算时间只比CSS角点检测方法多0.1 s与0.9 s，相对CSS算法计算时间未有显著增加。结论 该方法采用较低的曲率尺度可检测出更多的角点，降低了角点漏检测率；通过计算椭圆角点自适应阈值删除椭圆角点，并采用Freeman链码方向统计方法剔除伪角点，提高了角点检测精度。实验结果表明，本文提出的角点检测方法比其他角点检测算法具有高效性和准确性。

Objective Corners are an important feature of objects in images and they are widely used. In the field of computer vision. There are many applications that rely on the successful detection of corners,such as image matching,panoramic stitching,3D modelling,object recognition,and motion tracking. The multi-scale algorithm based on curvature scale space is generally regarded as an effective method for finding corners in images. It can detect both fine and coarse features. However,different options of the selected scale will result in the leakage detection of the corner or even the wrong corner. Therefore,we describe a new corner detection method based on the curvature scale space and the direction of Freeman code statistics. Method This corner detection method is based on the improved curvature scale space corner detection algorithm proposed by He et al. First,it uses the Canny edge detector to get the binary edge map,and computes the curvature at a relatively low scale for each edge. It can pick the candidate corners set in this relatively low scale. In this way, it picks enough corners, unfortunately also containing wrong corners,such as round corners and false corners which are caused by the sharp variation edges. Then,in order to remove those wrong corners, we propose an adaptive threshold and statistical direction of chain code method. We compute a threshold adaptively according to the mean curvature within a specific region. Round corners are removed by comparing the curvature of the corner candidates with the adaptive threshold. We also eliminate the false corners by evaluating the difference of each candidate corner,the difference evaluating by using the Freeman code direction statistics. Result In the conventional corner detection based on curvature scale space,different options of the selected scale will result in the leakage detection of the corner or even the wrong corner. For example using the high scale easily leads to real corners missing,using the low scale will detect more wrong corners. In order to find more real corners,this method uses the low scale to detect corners and proposes a novel method to remove those wrong corners. The evaluation of this algorithm is taken from several aspects,such as the accuracy of detection,the wrong detection,miss detection and the computing time.The experimental results show that this proposed corner detection method overcomes others methods weakness such as accuracy,false corners and so on. Compared with other algorithms,the new method has high accuracy,low error rate and there is not a significant increase in computing time. Conclusion We propose a novel method for detecting corners. This method adopts the low curvature scale and has the capacity of detecting more corners and accordingly reducing corner leakage detection rate;by calculating elliptical corner adaptive threshold,it can delete elliptical corner;by using the Freeman code direction statistics,it can also eliminate the false corner;thus undoubtedly improving the accuracy of corner detection. The experiments have fully prored that the corner detection algorithm we proposed in this paper has high efficiency and accuracy compared with other methods.