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Proceedings Paper

Automatic x-ray image segmentation and clustering for threat detection
Author(s): Odysseas Kechagias-Stamatis; Nabil Aouf; David Nam; Carole Belloni
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Paper Abstract

Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers’ attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture’s promising performance.

Paper Details

Date Published: 5 October 2017
PDF: 9 pages
Proc. SPIE 10432, Target and Background Signatures III, 104320O (5 October 2017); doi: 10.1117/12.2277190
Show Author Affiliations
Odysseas Kechagias-Stamatis, Cranfield Univ. (United Kingdom)
Nabil Aouf, Cranfield Univ. (United Kingdom)
David Nam, Cranfield Univ. (United Kingdom)
Carole Belloni, Cranfield Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 10432:
Target and Background Signatures III
Karin U. Stein; Ric Schleijpen, Editor(s)

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