仅用一张图像预测材料微结构代表性，省去大量数据需求
Prediction of microstructural representativity from a single image
- 基于两点关联函数，从单张图像直接估算方差
- 在多个开源数据集上验证，准确预测相分数与置信度
- 适合数据有限的材料科学家快速评估微结构代表性
本研究提出一种方法，可从单张材料微结构图像（2D或3D）中预测相分数的代表性。传统方法通常需要大规模数据集和复杂的统计分析来估计积分范围（Integral Range），这是决定微结构属性方差的关键因素。本文方法利用两点关联函数，直接从单张图像估算方差，从而实现相分数的预测并附带置信水平。我们使用多个开源数据集验证了该方法的有效性，结果表明其在多种微结构类型中均表现良好。该技术显著降低了代表性分析的数据需求，为材料科学家和工程师提供了实用工具，尤其适用于微结构数据稀缺的情况。为便于应用，我们开发了网页工具 www.imagerep.io，支持快速、简便且信息丰富的使用。
原文摘要 · Abstract (English)
In this study, we present a method for predicting the representativity of the phase fraction observed in a single image (2D or 3D) of a material. Traditional approaches often require large datasets and extensive statistical analysis to estimate the Integral Range, a key factor in determining the variance of microstructural properties. Our method leverages the Two-Point Correlation function to directly estimate the variance from a single image, thereby enabling phase fraction prediction with associated confidence levels. We validate our approach using open-source datasets, demonstrating its efficacy across diverse microstructures. This technique significantly reduces the data requirements for representativity analysis, providing a practical tool for material scientists and engineers working with limited microstructural data. To make the method easily accessible, we have created a web-application, www.imagerep.io, for quick, simple and informative use of the method.
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