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Proceedings Paper

Classification-free threat detection based on material-science-informed clustering
Author(s): Siyang Yuan; Scott D. Wolter; Joel A. Greenberg
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Paper Abstract

X-ray diffraction (XRD) is well-known for yielding composition and structural information about a material. However, in some applications (such as threat detection in aviation security), the properties of a material are more relevant to the task than is a detailed material characterization. Furthermore, the requirement that one first identify a material before determining its class may be difficult or even impossible for a sufficiently large pool of potentially present materials. We therefore seek to learn relevant composition-structure-property relationships between materials to enable material-identification-free classification. We use an expert-informed, data-driven approach operating on a library of XRD spectra from a broad array of stream of commerce materials. We investigate unsupervised learning techniques in order to learn about naturally emergent groupings, and apply supervised learning techniques to determine how well XRD features can be used to separate user-specified classes in the presence of different types and degrees of signal degradation.

Paper Details

Date Published: 1 May 2017
PDF: 8 pages
Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870K (1 May 2017); doi: 10.1117/12.2262942
Show Author Affiliations
Siyang Yuan, Duke Univ. (United States)
Scott D. Wolter, Elon Univ. (United States)
Joel A. Greenberg, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 10187:
Anomaly Detection and Imaging with X-Rays (ADIX) II
Amit Ashok; Edward D. Franco; Michael E. Gehm; Mark A. Neifeld, Editor(s)

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