Robust image corner detection through curvature scale space
Robust image corner detection through curvature scale space
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DOI：
10.1109/34.735812
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发表时间：
1998-12-01
影响因子：
23.6
通讯作者：
Suomela, R
中科院分区：
文献类型：
--
作者：
Mokhtarian, F;Suomela, R
本文描述了一种基于曲率尺度空间（CSS）表示的图像角点检测的新方法。第一步是使用 Canny 检测器从原始图像中提取边缘。图像的角点被定义为图像边缘具有绝对曲率最大值的点。在 CSS 的高比例下检测角点，并通过多个较低比例进行跟踪以改进定位。这种方法对噪声非常鲁棒，我们相信它比现有的角点检测器表现更好。还提出了 Canny 边缘检测器对 45 度和 135 度边缘响应的改进。此外，除了传统的角点之外，CSS 检测器还可以提供额外的点特征（图像边缘轮廓的曲率过零）。
This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector. The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors. An improvement to Canny edge detector's response to 45 degrees and 135 degrees edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners.