Emergence and distinction of classes in XRD data via machine learning

Published

Conference Paper

© 2019 SPIE. Downloading of the abstract is permitted for personal use only. The material-specific information contained in X-ray diffraction (XRD) measurements make it attractive for the detection of threats in airport baggage. Spatially-localized XRD signatures at each voxel in a bag may be obtained with a snapshot via coded aperture XRD tomography, but measurement unceratinty due to data processing and low SNR can lead to loss in information. We use machine learning and non-linear dimension reduction to identify threat and non-threat items in a way that overcomes these variations in the data. We observe the emergence of clusters from the data, possibly providing new prospects for XRD-based classification. We further show improved performance using machine learning methods relative to a conventional, correlation-based classifier in the low-SNR regime.

Full Text

Duke Authors

Cited Authors

  • Royse, C; Wolter, S; Greenberg, JA

Published Date

  • January 1, 2019

Published In

Volume / Issue

  • 10999 /

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9781510626638

Digital Object Identifier (DOI)

  • 10.1117/12.2519500

Citation Source

  • Scopus