On the Convergence of Planar Curves Under Smoothing

On the Convergence of Planar Curves Under Smoothing
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关于平滑下平面曲线的收敛性

DOI:
10.1109/tip.2010.2046807
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发表时间:
2010-08
影响因子:
10.6
通讯作者:
Zhong, Baojiang
Zhong, Baojiang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Ma, Kai-Kuang;Zhong, Baojiang

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曲线平滑在计算机视觉和图像处理中有两个重要的应用:1)用于形状分析的曲率尺度空间(CSS)技术;2)用于噪声抑制的高斯滤波。在这篇文章中,我们研究了平面曲线如何随着比例的增加而被平滑而收敛。首先,阐明了两种收敛行为。收缩是指平滑的平面曲线的弧长的减小,它在纬向上描述了曲线的收敛;另一个虚构的术语收缩是指每个点向其极限位置的运动,它在纵向上描述了曲线的收敛。然后对三类曲线模型的收缩和崩溃进行了系统的研究。拐角模型有助于揭示平面曲线的局部结构是如何崩溃的,以及平滑后的曲线可能收敛到什么位置。锯齿模型使我们能够深入了解如何通过高斯滤波器从有噪声的平面曲线中抑制噪声。我们对闭合曲线的研究表明,每条曲线都折叠到其质心处的一点。但是,不同的曲线可能会在无穷大范围内产生不同的极限形状。最后,在此基础上分析了该方法在角点检测和形状表示方面的性能,并提出了一种基于高斯滤波的噪声抑制的快速实现方法。
Curve smoothing has two important applications in computer vision and image processing: 1) the curvature scale-space (CSS) technique for shape analysis, and 2) the Gaussian filter for noise suppression. In this paper, we study how planar curves converge as they are smoothed with increasing scales. First, two types of convergence behavior are clarified. The coined term shrinkage refers to the reduction of arc-length of a smoothed planar curve, which describes the convergence of the curve latitudinally; and another coined term collapse refers to the movement of each point to its limiting position, which describes the convergence of the curve longitudinally. A systematic study on the shrinkage and collapse of three categories of curve models is then presented. The corner models helps to reveal how the local structures of planar curves collapse and what the smoothed curves may converge to. The sawtooth models allows us to gain insights regarding how noise is suppressed from noisy planar curves by the Gaussian filter. Our investigation on the closed curves shows that each curve collapses to a point at its center of mass. However, different curves may yield different limiting shapes at the infinity scale. Finally, based upon the derived results the performance of the CSS technique in corner detection and shape representation is analyzed, and a fast implementation method of the Gaussian filter for noise suppression is proposed.
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