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

Yawei Hui; Yaohua Liu

doi:10.1007/978-3-030-17795-9_18

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

Yawei Hui, Yaohua Liu

STS Project Technical Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
Editors	Supriya Kapoor, Kohei Arai
Publisher	Springer Verlag
Pages	257-271
Number of pages	15
ISBN (Print)	9783030177942
DOIs	https://doi.org/10.1007/978-3-030-17795-9_18
State	Published - 2020
Event	Computer Vision Conference, CVC 2019 - Las Vegas, United States Duration: Apr 25 2019 → Apr 26 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	943
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Computer Vision Conference, CVC 2019
Country/Territory	United States
City	Las Vegas
Period	04/25/19 → 04/26/19

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Feature extraction
Scientific visualization
Unsupervised learning and clustering
Volumetric dataset

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10.1007/978-3-030-17795-9_18

Cite this

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title = "Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science",

abstract = "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.",

keywords = "Feature extraction, Scientific visualization, Unsupervised learning and clustering, Volumetric dataset",

author = "Yawei Hui and Yaohua Liu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Computer Vision Conference, CVC 2019 ; Conference date: 25-04-2019 Through 26-04-2019",

year = "2020",

doi = "10.1007/978-3-030-17795-9_18",

language = "English",

isbn = "9783030177942",

series = "Advances in Intelligent Systems and Computing",

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}

Hui, Y & Liu, Y 2020, Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science. in S Kapoor & K Arai (eds), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Advances in Intelligent Systems and Computing, vol. 943, Springer Verlag, pp. 257-271, Computer Vision Conference, CVC 2019, Las Vegas, United States, 04/25/19. https://doi.org/10.1007/978-3-030-17795-9_18

Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science. / Hui, Yawei; Liu, Yaohua.
Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. ed. / Supriya Kapoor; Kohei Arai. Springer Verlag, 2020. p. 257-271 (Advances in Intelligent Systems and Computing; Vol. 943).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Unsupervised learning and clustering

KW - Volumetric dataset

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

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