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Accurate Estimation of Fractal Dimension of Binary Images by Box-Counting Method with Automatic Scale Selection

机译:利用自动比例尺选择的盒数法精确估计二值图像的分形维数

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摘要

Fractal dimension can be used to describe the self-similarity, the complexity, or the irregularity of images. One of the most popular ways to estimate the fractal dimension of images is the box-counting method (BCM). Its naive estimates, however, tend to be inaccurate. This paper gives careful consideration to the sources of inaccurate estimates and modifies Buczkowski's method for accurate estimation of the fractal dimension of binary images. Buczkowski's method is the modified version of the BCM and can provide the most accurate estimates of all the methods based on the BCM so far. The proposed method automatically eliminates the scales which cause the degradation of estimation accuracy after the box-counting stage for whole scales available from a binary image. And then the method fits a regression line to the selected points on a log-log plot in order to obtain the estimate of the fractal dimension. Preliminary consideration indicates that the proposed method is less time-consuming than Buczkowski's method. Some experiments with deterministic fractals, random fractals, and Euclidean objects are also conducted to compare the proposed method to conventional ones in terms of estimation accuracy. The results show that the proposed method enables us to obtain more accurate estimates of the fractal dimension of binary images than the BCM and Buczkowski's method.
机译:分形维数可用于描述图像的自相似性,复杂性或不规则性。估计图像的分形维数最流行的方法之一是盒计数法(BCM)。但是,其天真的估计往往不准确。本文仔细考虑了不准确估计的来源,并修改了Buczkowski的方法来准确估计二值图像的分形维数。 Buczkowski的方法是BCM的修改版本,可以提供到目前为止基于BCM的所有方法的最准确的估计。所提出的方法自动消除了标度,这些标度在框计数阶段之后可用于二值图像的整个标度,从而导致估计精度下降。然后,该方法将回归线拟合到对数对数图上的选定点,以便获得分形维数的估计值。初步考虑表明,所提出的方法比Buczkowski的方法耗时少。还进行了一些确定性分形,随机分形和欧几里德对象的实验,以从估计精度的角度将本方法与常规方法进行比较。结果表明,与BCM和Buczkowski方法相比,所提出的方法使我们能够更准确地估计二值图像的分形维数。

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