Sterbentz Randy M, Haley Kristine L, Island Joshua O
Department of Physics and Astronomy, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA.
Sci Rep. 2021 Mar 11;11(1):5808. doi: 10.1038/s41598-021-85159-9.
Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.
机器学习方法正在改变数据分析的方式。这些技术最强大且应用最广泛的领域之一是图像分割，即在数字图像中对不同的对象进行划分和分类。在此，我们展示了一个图像分割程序，该程序结合了一系列无监督聚类算法，用于从数字光学显微镜图像中自动识别二维材料的厚度。该程序能够在不透明和透明基板上识别多种材料的单层和少层薄片，像素精度约为95%。与之前的尝试不同，通过保留和分析所有三个数字颜色通道以及对任意形状的数据簇进行高斯混合模型拟合，实现了应用的通用性。我们的结果为在任何基板上剥落的二维材料的通用自动识别提供了一种简便的数据聚类实现方法。
