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摘要: 提出了一种快速的骨架提取算法.该方法首先在轮廓离散曲线演化的基础上，根据显著凸顶点的类型将轮廓多边形进行分块，得到一个主分支轮廓和多个水平分支轮廓；然后分别利用水平截线法和垂直截线法提取骨架的主分支和水平分支；最后将水平分支拼接在主分支上，得到完整的骨架.实验结果表明，该骨架提取算法可以得到连通的骨架，并在Kimia数据集上取得了较好的效果.此外，算法在自然图像上的效果也很好，尤其适用于视频中的行人骨架提取.与经典骨架提取算法相比，该算法的时间复杂度较低，可以满足实时处理的要求.Abstract: A fast skeleton extraction algorithm is proposed in this paper. Firstly, some vertices of the polygon contour are labeled as salient convex vertices by using discrete curve evolution, then the polygon contour is partitioned into a main branch and several horizontal branches according to the types of these salient convex vertices. Secondly, the horizontal secant lines method is used to get the skeleton of the main branch and the vertical secant line method is used to obtain the skeletons of horizontal branches separately. Finally, by connecting the skeletons of horizontal branches to main branch skeleton, the final skeleton of a given contour is obtained. Experimental results show that the proposed skeleton extraction algorithm has good performance on Kimia data set. In addition, results on pedestrian video are presented to show that the proposed algorithm performs well on natural images, too. Furthermore, the time complexity of the algorithm is lower than those of other classical algorithms and it satisfies the requirement of real-time processing of videos.
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图 7 不同的K0值得到的原始轮廓和顶点个数N
Fig. 7 Original contour with different vertices number N according to different K0
图 11 放大1, 5, 10倍实验结果
Fig. 11 Results of skeletonization with original image, magnified 5 times and magnified 10 times
表 1 图像骨架提取的运行时间
Table 1 The running time of skeletonization
图片大小 原始轮廓顶点数 轮廓提取 轮廓简化 离散曲线演化 截线法提取骨架 1 560×2 250 875 0.0171 s 0.0305 s 0.0045 s 0.1294 s 
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表 2 不同尺寸图片骨架提取的运行时间对比
Table 2 The comparison of running time of skeletonization of different image sizes
图片大小 本文算法 文献[32]算法运行时间 原始轮廓顶点数 运行时间 1 156×225 51 0.0036 s 8.0328 s 2 312×450 100 0.0089 s 18.6070 s 3 468×675 100 0.0125 s 38.2181 s 4 624×900 309 0.0506 s 64.4884 s 5 780×1 125 327 0.0643 s 111.5206 s 6 936×1 350 412 0.0741 s 218.3063 s 7 1 092×1 575 437 0.0922 s 267.5379 s 8 1 248×1 800 598 0.1348 s 410.8053 s 9 1 404×2 025 783 0.1572 s 545.5659 s 10 1 560×2 250 875 0.2128 s 733.5245 s
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