当前位置: X-MOL 学术Comp. Mater. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Unsupervised segmentation of microstructural images of steel using data mining methods
Computational Materials Science ( IF 3.3 ) Pub Date : 2021-09-22 , DOI: 10.1016/j.commatsci.2021.110855
Hoheok Kim 1 , Yuuki Arisato 2 , Junya Inoue 1, 2
Affiliation  

A novel and efficient data mining method for the segmentation of microstructural images of low-carbon steel is presented. Microstructural characterization has been the focus of many works in the field of materials science because microstructure is the fundamental element in understanding the link between process and property. Recently, deep-learning-based methods have been actively employed for microstructural classification since it has shown outstanding performance for solving image classification problems. However, previous applications of deep learning models to microstructural classification revealed limitations in that not only do they require the time-consuming labeling process but it is also still difficult to obtain a satisfactory result, especially for steel microstructures containing substances developed by displacive transformation. In this study, we propose a rule-based segmentation method that not only works without labeled images but also requires no prior knowledge of the number of microstructural constituents in each image. This unsupervised inference algorithm captures the morphological features of each microstructure and automatically finds the optimal number of microstructures having similar characteristics using a Bayesian Gaussian mixture model. The viability of our method is demonstrated by qualitative and quantitative evaluations with optical microscopy images of steel composed of different microstructures taken under different imaging conditions.



中文翻译:

使用数据挖掘方法对钢的微观结构图像进行无监督分割

提出了一种用于低碳钢显微组织图像分割的新型高效数据挖掘方法。微观结构表征一直是材料科学领域许多工作的重点,因为微观结构是理解工艺和性能之间联系的基本要素。最近,基于深度学习的方法已被积极用于微观结构分类,因为它在解决图像分类问题方面表现出出色的性能。然而,以往深度学习模型在微观结构分类中的应用暴露出局限性,它们不仅需要耗时的标记过程,而且仍然难以获得令人满意的结果,尤其是对于含有由位移转变产生的物质的钢微观结构。在这项研究中,我们提出了一种基于规则的分割方法,它不仅可以在没有标记图像的情况下工作,而且不需要每个图像中微观结构成分数量的先验知识。这种无监督推理算法捕捉每个微结构的形态特征,并使用贝叶斯高斯混合模型自动找到具有相似特征的微结构的最佳数量。我们的方法的可行性通过定性和定量评估来证明,其中钢的光学显微镜图像由在不同成像条件下拍摄的不同微观结构组成。这种无监督推理算法捕获每个微结构的形态特征,并使用贝叶斯高斯混合模型自动找到具有相似特征的微结构的最佳数量。我们的方法的可行性通过定性和定量评估来证明,其中钢的光学显微镜图像由在不同成像条件下拍摄的不同微观结构组成。这种无监督推理算法捕获每个微结构的形态特征,并使用贝叶斯高斯混合模型自动找到具有相似特征的微结构的最佳数量。我们的方法的可行性通过定性和定量评估来证明,其中钢的光学显微镜图像由在不同成像条件下拍摄的不同微观结构组成。

更新日期:2021-09-23
down
wechat
bug