渐近分形集维数的一种估计方法

Gabriel Landini, Jean Paul Rigaut
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引用次数: 0

摘要

分形几何的发展促使人们使用物体的分形维数作为形态复杂性的度量。许多生物标本呈现分形尺度,但尺度范围有限。超出这些限制,这些标本是欧几里得的。这样的对象被称为渐近分形,并提出了替代模型来描述它们。这些方法依赖于用长度分辨率技术(例如“尺度”方法)收集的数据与渐近分形标度的理论模型的拟合。不幸的是,用所谓的“盒子计数”方法产生的数据不能用于这些模型。我们报告了一种新的方法来估计渐近分形的渐近分形行为(在低分辨率下)基于单一的假设,即样品接近欧几里得物体在高分辨率。所描述的过程可以应用于长度分辨方法以及盒计数方法,并为估计簇和分支结构中的渐近分形行为提供了可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method for estimating the dimension of asymptotic fractal sets

The development of fractal geometry has prompted the use of fractal dimensions of objects as measures of morphological complexity. Many biological specimens show fractal scaling, but within limited scale ranges. Beyond those limits, the specimens are Euclidean. Such objects are called asymptotic fractals and alternative models have been proposed to describe them. These approaches rely on fitting data gathered with length-resolution techniques (for example the ‘yardstick’ method) to theoretical models of asymptotic fractal scaling. Unfortunately, data produced with the so-called ‘box counting’ method cannot be used with these models. We report a new approach to estimate the asymptotic fractal behaviour (at low resolution) of asymptotic fractals based on the single assumption that the specimen approaches a Euclidean object at high resolutions. The procedure described can be applied using length-resolution methods as well as the box counting method and opens the possibility for estimating asymptotic fractal behaviour in both cluster and branching structures.

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