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Estimation of fractal dimension and fractal curvatures from digital images

机译:从数字图像估计分形维数和分形曲率

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摘要

Most of the known methods for estimating the fractal dimension of fractal sets are based on the evaluation of a single geometric characteristic, e.g. the volume of its parallel sets. We propose a method involving the evaluation of several geometric characteristics, namely all the intrinsic volumes (i.e. volume, surface area, Euler characteristic, etc.) of the parallel sets of a fractal. Motivated by recent results on their limiting behavior, we use these functionals to estimate the fractal dimension of sets from digital images. Simultaneously, we also obtain estimates of the fractal curvatures of these sets, some fractal counterpart of intrinsic volumes, allowing a finer classification of fractal sets than by means of fractal dimension only. We show the consistency of our estimators and test them on some digital images of self-similar sets. (C) 2015 Elsevier Ltd. All rights reserved.
机译:估计分形集的分形维数的大多数已知方法都是基于对单个几何特征(例如,几何形状)的评估。其并行集的体积。我们提出了一种方法,该方法涉及评估几个几何特征,即分形的平行集的所有本征体积(即体积,表面积,欧拉特征等)。受近期关于限制行为的结果的启发,我们使用这些功能从数字图像中估计集合的分形维数。同时,我们还获得了这些集合的分形曲率的估计值,这是本征体积的一些分形对应物,与仅通过分形维数相比,可以对分形集进行更好的分类。我们展示了估计量的一致性,并在一些自相似集合的数字图像上对其进行了测试。 (C)2015 Elsevier Ltd.保留所有权利。

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