Unsupervised Clustering Methods for Image Segmentation: Application to SEM Images of Graphene

2021.01.13 Aagam Shah, University of Illinois at Urbana-Champaign

In digital image processing and computer vision, image segmentation refers to the process of partitioning a digital image into multiple segments or related sets of pixels. This tutorial will introduce you to some basic image segmentation techniques driven by unsupervised machine learning techniques such as the Gaussian mixture model and k-means clustering. You will learn how to implement k-means clustering and template matching, and use these to segment a scanning electron microscopy image of graphene on a substrate.