Zhang Weichuan, Sun Changming, Breckon Toby, Alshammari Naif
IEEE Trans Image Process. 2019 Sep;28(9):4444-4459. doi: 10.1109/TIP.2019.2910655. Epub 2019 Apr 17.
Image corner detection is very important in the fields of image analysis and computer vision. Curvature calculation techniques are used in many contour-based corner detectors. We identify that existing calculation of curvature is sensitive to local variation and noise in the discrete domain and does not perform well when corners are closely located. In this paper, discrete curvature representations of single and double corner models are investigated and obtained. A number of model properties have been discovered, which help us detect corners on contours. It is shown that the proposed method has a high corner resolution (the ability to accurately detect neighboring corners), and a corresponding corner resolution constant is also derived. Meanwhile, this method is less sensitive to any local variations and noise on the contour; and false corner detection is less likely to occur. The proposed detector is compared with seven state-of-the-art detectors. Three test images with ground truths are used to assess the detection capability and localization accuracy of these methods in cases with noise-free and different noise levels; 24 images with various scenes without ground truths are used to evaluate their repeatability under affine transformation, JPEG compression, and noise degradations. The experimental results show that our proposed detector attains a better overall performance.
图像角点检测在图像分析和计算机视觉领域非常重要。曲率计算技术被用于许多基于轮廓的角点检测器中。我们发现，现有的曲率计算在离散域中对局部变化和噪声敏感，并且在角点相距很近时表现不佳。本文研究并获得了单角点模型和双角点模型的离散曲率表示。发现了许多模型属性，这些属性有助于我们在轮廓上检测角点。结果表明，所提出的方法具有较高的角点分辨率（准确检测相邻角点的能力），并且还推导了相应的角点分辨率常数。同时，该方法对轮廓上的任何局部变化和噪声不太敏感；并且不太可能发生误角点检测。将所提出的检测器与七个最先进的检测器进行了比较。使用三张带有真实值的测试图像来评估这些方法在无噪声和不同噪声水平情况下的检测能力和定位精度；使用24张没有真实值的各种场景的图像来评估它们在仿射变换、JPEG压缩和噪声退化下的可重复性。实验结果表明，我们提出的检测器获得了更好的整体性能。
