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Proceedings Paper

Emergence and distinction of classes in XRD data via machine learning
Author(s): Camen Royse; Scott Wolter; Joel A. Greenberg
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Paper Abstract

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.

Paper Details

Date Published: 14 May 2019
PDF: 8 pages
Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990D (14 May 2019); doi: 10.1117/12.2519500
Show Author Affiliations
Camen Royse, Duke Univ. (United States)
Scott Wolter, Elon Univ. (United States)
Joel A. Greenberg, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 10999:
Anomaly Detection and Imaging with X-Rays (ADIX) IV
Amit Ashok; Joel A. Greenberg; Michael E. Gehm, Editor(s)

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