About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
A Walk Through Material Microstructures: Using Deep Learning and Geometry to Better Visualize Large Collections of Material Microstructure Images |
Author(s) |
Jordan Weaver, Henry Kvinge |
On-Site Speaker (Planned) |
Henry Kvinge |
Abstract Scope |
The increasing rate at which material microstructure imagery is being collected has led to a need for better tools to explore and understand these large datasets. This means understanding not only individual images, but also relationships between collections of images from multiple samples. A common approach to achieving this understanding is to leverage data-driven visualizations that represent relationships spatially. Unfortunately, image data is generally very high-dimensional, and thus an image dataset can have complicated structure that cannot be captured in the 2 or 3-dimensions that the human visual system is limited to. We describe a method called DeepStroll that we have developed to overcome this challenge. Our method combines state-of-the-art computer vision models and principles from differential geometry to construct a 'tour' through an image dataset. We use the temporal component of the resulting video to capture structure in the dataset that cannot be summarized in a single visualization. |
Proceedings Inclusion? |
Undecided |