Universal image segmentation for optical identification of 2D materials.

ABSTRACT: 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 arbitraril

SUBMITTER: Sterbentz RM 

PROVIDER: S-EPMC7970966 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature