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.

Hand-on Tutorial (PDF)
Pre-Workshop Tutorial: Setting up a nanoHUB account and generating API keys for databases (PDF)

The nanoHUB tool "SEM Image Segmentation Workshop" used in this hands-on tutorial.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Aagam Rajeev Shah (2021), "Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene," https://nanohub.org/resources/34738.

  BibTex | EndNote

1. graphene
2. hands on
3. image processing
4. image segmentation
5. k-means clustering
6. machine learning
7. materials science
8. nanoMFG Node
9. NCN Group - Machine Learning
10. NCN Group - Machine Learning Hands-on
11. scanning electron microscopy (SEM)
12. tutorial
13. unsupervised learning