基于eikonal的随机镶嵌模型

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
B. Figliuzzi
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引用次数: 8

摘要

在本文中,我们提出了一种新的、有效的方法来计算随机镶嵌,从其在离散域的每个体素的向量表示。该方法基于Eikonal方程的解析,复杂度为O(N log N), N为用于离散域的体素数。相比之下,在一般情况下,在每个体素位置评估向量表示的隐式函数的复杂性为O(N²)。该方法还使我们能够通过模拟细菌在速度局部变化的区域上的生长来考虑细胞之间粗糙界面的镶嵌的产生。这方面构成了文章的主要贡献。最后的贡献是开发了一种算法,用于估计镶嵌细胞边界的多尺度扭曲度。该算法通过对边界进行迭代变形,使其成为一条直线,从而在多个尺度上计算边界的扭曲度。利用该算法,我们证明了依靠局部速度模型,可以控制单元边界的粗糙度幅度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EIKONAL-BASED MODELS OF RANDOM TESSELLATIONS
In this article, we propose a novel, efficient method for computing a random tessellation from its vectorial representation at each voxel of a discretized domain. This method is based upon the resolution of the Eikonal equation and has a complexity in O(N log N), N being the number of voxels used to discretize the domain. By contrast, evaluating the implicit functions of the vectorial representation at each voxel location has a complexity of O(N²) in the general case. The method also enables us to consider the generation of tessellations with rough interfaces between cells by simulating the growth of the germs on a domain where the velocity varies locally. This aspect constitutes the main contribution of the article. A final contribution is the development of an algorithm for estimating the multi-scale tortuosity of the boundaries of the tessellation cells. The algorithm computes the tortuosity of the boundary at several scales by iteratively deforming the boundary until it becomes a straight line. Using this algorithm, we demonstrate that depending on the local velocity model, it is possible to control the roughness amplitude of the cells boundaries.
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来源期刊
Image Analysis & Stereology
Image Analysis & Stereology MATERIALS SCIENCE, MULTIDISCIPLINARY-MATHEMATICS, APPLIED
CiteScore
2.00
自引率
0.00%
发文量
7
审稿时长
>12 weeks
期刊介绍: Image Analysis and Stereology is the official journal of the International Society for Stereology & Image Analysis. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography.
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