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首页> 外文会议>Conference on Image Extraction, Segmentation, and Recognition Oct 22-24, 2001, Wuhan, China >Corner Detection Algorithm based on Multi-Feature
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Corner Detection Algorithm based on Multi-Feature

机译：基于多特征的角点检测算法

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摘要

The corner is an important local feature of the image. This paper proposes a new algorithm for corner detection by multi-feature―intensity and edge points. A fast adaptive SUSAN principle based on local gray-level feature is presented firstly for detecting candidate corners. This improved method can detect the features in different contrast images automatically and fast through a self-adjust threshold, which is determined by the intensity gradient magnitude. To detect the corners on blurry edges, the candidate corners would include some edge points as a result of reducing the detection threshold. After these candidate corners are arrayed along the boundary trend by the method of edge element, the angles between approximate straight edge lines are calculated. Then the edge points contained in the candidate corners are removed since they have not significant discontinuous changes in the direction of boundary, and the false corners due to quantization are also removed by our method. In this way, the true corners are reserved. The experimental results showed that the proposed algorithm has good capability of corner detection and localization for different contrast image.
机译：角落是图像的重要局部特征。提出了一种基于强度和边缘点多特征的角点检测新算法。首先提出了一种基于局部灰度特征的快速自适应SUSAN原理，用于检测候选角点。这种改进的方法可以通过由强度梯度大小确定的自调整阈值自动快速检测不同对比度图像中的特征。为了检测模糊边缘上的拐角，由于降低了检测阈值，候选拐角将包括一些边缘点。通过边缘元素方法将这些候选角沿边界趋势排列后，计算出近似直线边缘之间的角度。然后删除候选角中包含的边缘点，因为它们在边界方向上没有明显的不连续变化，并且我们的方法还消除了由于量化导致的虚假角。这样，保留了真正的角落。实验结果表明，该算法对不同对比度的图像具有良好的拐角检测和定位能力。

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