多特征复合的角点提取方法

提出了一种利用图象灰度特征和边界轮廓点特征相复合的角点检测方法；首先在SUSAN特征检测原则基础上，提出了基于图象灰度特征的快速自适应特征检测方法，用以提取不同图象对比度下目标轮廓上的初角点，利用边沿元对这些包含了部分边缘点的初角点沿边绝缘方向跟踪排序后，再由根据图象边缘特征检测的边界方向变化情况来确定角点位置，同时剔除由于图象数字化而导致的虚假角点，这种方法克服了单一特征提取角点所带来的弊病，提高了角点检测的精度，抗噪能力强，运算量较小，适于实时实现。

Corner Detection Based on Multi-Feature

The corner is an important local feature of image. To avoid the disadvantages of using the single feature to detect the corner points, a new algorithm based on multi feature is proposed in this paper. In this algorithm, intensity feature and edge feature are contributed to corner detection. First, a fast adaptive SUSAN principle, which utilizes the local gray level feature directly, is proposed for detecting the candidate corners. This improved method can detect features, such as corners, edges and intersections, in different contrast image automatically. For detecting the corners on blurry edges, the candidate corners would include some edge points as a result of reducing the detection threshold. These candidate corners, which include true corners, some edge points and a few false points, are arrayed along the boundary trend by the method of edge element. Through these arrayed points, the angles between approximate straight edge lines are calculated to be as the criterion of determining a corner. Those edge points are removed since they have not significant discontinuous changes in the direction of boundary, i.e. the angles of them are not acute enough, and the false corners due to quantization also are removed by our method. After these steps, the true corners are reserved. The experimental results showed this corner detection method having good capabilities of detection and localization in different contrast image.