用最小距离估计求解Wicksell方程

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY Image Analysis & Stereology Pub Date : 2019-12-13 DOI:10.5566/ias.2133
D. Depriester, R. Kubler
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引用次数: 8

摘要

颗粒材料的粒度估计通常是通过二维观测来完成的。从二维表观尺寸展开晶粒尺寸分布通常被称为微粒问题。对于球形颗粒,由于威克塞尔方程,表观尺寸的分布可以与实际尺寸的分布联系起来。基于Wicksell方程的Saltikov方法是解决微粒问题最广泛使用的方法。该方法是递归的,适用于粒度的有限直方图。本文提出了一种基于最小化过程的Wicksell方程的数值求解算法,该算法假设Wicksell方程的分布是一个参数模型(如对数正态分布)。该算法应用于真实材料,并将结果与使用Saltikov或基于Saltikov的立体技术得到的结果进行比较。提出了萨尔提科夫方法中选择箱数的准则。研究了该算法在不同样本量下的准确性。
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Resolution of the Wicksell's equation by Minimum Distance Estimation
The estimation of the grain size in granular materials is usually performed by 2D observations. Unfolding the grain size distribution from apparent 2D sizes is commonly referred as the corpuscle problem. For spherical particles, the distribution of the apparent size can be related to that of the actual size thanks to the Wicksell’s equation. The Saltikov method, which is based on Wicksell’s equation, is the most widely used method for resolving corpuscle problems. This method is recursive and works on the finite histogram of the grain size. In this paper, we propose an algorithm based on a minimizing procedure to numerically solve the Wicksell’s equation, assuming a parametric model for the distribution (e.g. lognormal distribution). This algorithm is applied on real material and the results are compared to those found using Saltikov or Saltikov-based stereology techniques. A criterion is proposed for choosing the number of bins in the Saltikov method. The accuracy of the proposed algorithm, depending on the sample size, is studied.
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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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