基于支持向量机的材料物相分类
Material phase classification by means of Support Vector Machines
摘要：
从随机异质材料获得的图像的像素分类是在作为随机重建的表征过程期间计算其物理性质（如有效传输系数（ETC））的相关步骤。一个坏的分类将影响计算的属性;然而，关于这一主题的文献主要讨论了关联函数或属性公式，很少或根本没有注意到分类;作者提到使用阈值或在少数情况下使用大津方法。本文提出了一种基于支持向量机（SVM）的分类方法，并与大津的方法进行了比较，基于准确性和精度。用于SVM训练的数据是更好分类的关键，这些数据是灰度值，像素梯度的大小和方向。例如，在案例研究中，像素的分类准确率为77.6%的支持向量机方法和40.9%的大津的方法。最后，讨论了对关联函数的影响，以显示该建议的好处。
英文摘要：
The pixel's classification of images obtained from random heterogeneous materials is a relevant step to compute their physical properties, like Effective Transport Coefficients (ETC), during a characterization process as stochastic reconstruction. A bad classification will impact on the computed properties; however, the literature on the topic discusses mainly the correlation functions or the properties formulae, giving little or no attention to the classification; authors mention either the use of a threshold or, in few cases, the use of Otsu's method. This paper presents a classification approach based on Support Vector Machines (SVM) and a comparison with the Otsu's-based approach, based on accuracy and precision. The data used for the SVM training are the key for a better classification; these data are the grayscale value, the magnitude and direction of pixels gradient. For instance, in the case study, the accuracy of the pixel's classification is 77.6% for the SVM method and 40.9% for Otsu's method. Finally, a discussion about the impact on the correlation functions is presented in order to show the benefits of the proposal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
