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Skeletonization using the divergence of an anisotropic vector field flow

机译:骨骼化使用各向异性矢量场流的分歧

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In this paper we introduce a procedure to obtain both the automatic positioning of an initial contour for edge extraction and a new approach to skeletonize the captured contour. We propose a parametric deformable method that relies on a generalized anisotropic flow for the evaluation of an external force field which will be applied on active contour procedure. Therefore we recur to field divergence to analyze its convergence, in order to better place an initial curve. We put in evidence that the divergence of the vector field satisfies an anisotropic diffusion equation as well. The curves of positive divergence may be thought as propagating fronts of an evolution function. It has been proved that the sets of points in the image domain where divergence assumes positive values, converge to the skeleton of the extracted contour. We suggest that the divergence of a vector field may be an alternative medial axis function as soon the steady configuration of the anisotropic flow has been reached.
机译:在本文中,我们介绍了一种过程,以获得初始轮廓的自动定位,以获得边缘提取和新方法来克服捕获的轮廓。我们提出了一种参数可变形方法,其依赖于用于评估将应用于主动轮廓过程的外力场的广义各向异性流。因此,我们转发田间分歧以分析其融合,以便更好地放置初始曲线。我们提出了载体场的分歧,也满足了各向异性扩散方程。积极分歧的曲线可能被认为是传播进化功能的前线。已经证明,图像域中的点组在发散假定阳性值的情况下,收敛到提取的轮廓的骨架。我们建议,载体场的发散可以是替代的内侧轴功能,只要达到各向异性流的稳定配置。

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