On the generation and pruning of skeletons using generalized Voronoi diagrams
On the generation and pruning of skeletons using generalized Voronoi diagrams
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DOI：
10.1016/j.patrec.2012.07.014
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Hongzhi Liu;Zhonghai Wu;D. Hsu;B. Peterson;Dongrong Xu
中科院分区：
文献类型：
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作者：
Hongzhi Liu;Zhonghai Wu;D. Hsu;B. Peterson;Dongrong Xu
骨架化是图像处理和物体识别等多种应用中的必要过程。然而，骨架的概念最初是在连续空间中表述的，该骨架是使用最大圆盘中心的并集或具有多个生成点的点的并集来定义的。当它们应用于离散空间中的情况时，所得的骨架可能会变得断开，需要进一步的工作来链接它们。在本文中，我们提出了一种新颖的骨架化方法，该方法使用广义 Voronoi 图将骨架的概念扩展到包括连续空间和离散空间。我们还提出了一种骨架修剪方法，能够通过评估噪声分支的重要性来去除它们。三个实验结果表明：（1）我们的方法在各种形状上都是稳定的，（2）它比以前处理边界包含大量噪声的形状的方法在准确性和鲁棒性方面表现更好。
Skeletonization is a necessary process in a variety of applications in image processing and object recognition. However, the concept of a skeleton, defined using either the union of centers of maximal discs or the union of points with more than one generating points, was originally formulated in continuous space. When they are applied to situation in discrete space, the resulting skeletons may become disconnected and further works are needed to link them. In this paper, we propose a novel skeletonization method which extends the concept of a skeleton to include both continuous and discrete space using generalized Voronoi diagrams. We also present a skeleton pruning method which is able to remove noisy branches by evaluating their significance. Three experimental results demonstrate that: (1) our method is stable across a wide range of shapes, and (2) it performs better in accuracy and robustness than previous approaches for processing shapes whose boundaries contain substantial noise.