Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique
Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique
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DOI：
10.1109/tmm.2008.2001384
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发表时间：
2008-10-01
影响因子：
7.3
通讯作者：
Lu, Guojun
中科院分区：
文献类型：
--
作者：
Awrangjeb, Mohammad;Lu, Guojun
许多基于轮廓的图像角点检测器都是基于曲率尺度空间的。我们找出了基于css的检测器的弱点。首先，“曲率”本身的“定义”对曲线上的局部变化和噪声非常敏感，除非事先进行了适当的平滑处理。此外，曲率的计算涉及高达二阶的导数，这可能会导致结果不稳定和误差。其次，高斯平滑会引起曲线的变化，很难选择合适的平滑尺度，导致CSS角点检测技术的性能较差。提出了一种完整的基于弦到点距离累加(CPDA)的角点检测技术，用于离散曲率估计。CPDA离散曲率估计技术对曲线上的局部变化和噪声不太敏感。此外，它不具有高斯平滑的不良效果。我们提供全面的性能研究。实验结果表明，该方法在平均重复性和定位误差方面均优于现有的基于CSS的方法和其他相关方法。
Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the "curvature" itself by its "definition" is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.