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Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science...

by Yawei Hui, Yaohua Liu
Publication Type
Conference Paper
Journal Name
Proceedings of the Computer Vision Conference
Publication Date
Page Numbers
257 to 271
Conference Name
Computer Vision Conference (CVC) 2019
Conference Location
Las Vegas, Nevada, United States of America
Conference Sponsor
The Science and Information (SAI) Organization
Conference Date

Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 108–1010 data points), so that conventional volumetric visualization approaches become inefficient for both still imaging and interactive OpenGL rendition in a 3D setting. We introduce a new approach based on the unsupervised machine learning algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to efficiently analyze and visualize large volumetric datasets. Here we present two examples of analyzing and visualizing datasets from the diffuse scattering experiment of a single crystal sample and the tomographic reconstruction of a neutron scanning of a turbine blade. We found that by using the intensity as the weighting factor in the clustering process, DBSCAN becomes very effective in de-noising and feature/boundary detection, and thus enables better visualization of the hierarchical internal structures of the neutron scattering data.