Welcome back to our Materials Informatics playlist! In our previous video, we explored generative models like GANs. Today, we’re taking a brief interlude to discuss the fascinating world of image segmentation because tools from image segmentation are crucial to our next generative model!

In this video, we cover:

The basics of image segmentation and its importance in materials science.

Different types of segmentation: semantic segmentation, instance segmentation, and more.

Classical image segmentation techniques: thresholding, edge detection, and region growing. The revolutionary U-Net architecture, originally developed for biomedical image segmentation, and its application in materials science. The Segment Anything Model by Meta, the state-of-the-art in image segmentation technology.

Stay tuned to learn about how these techniques are transforming the way we analyze and understand material properties. If you're eager to dive into diffusion models, check out our next video where we explore how U-Net plays a pivotal role in that context.

Researchers should cite this work as follows:

• Taylor Sparks (2024), "Materials Informatics Lecture 28: Image Segmentation," https://nanohub.org/resources/39572.

  BibTex | EndNote

1. computational materials science
2. image segmentation
3. machine learning
4. material properties
5. materials informatics
6. materials science
7. neural networks