Automatic x-ray image segmentation and clustering for threat detection

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dc.contributor.authorKechagias-Stamatis, Odysseas
dc.contributor.authorAouf, Nabil
dc.contributor.authorNam, David
dc.contributor.authorBelloni, Carole
dc.date.accessioned2018-10-01T13:19:09Z
dc.date.available2018-10-01T13:19:09Z
dc.date.issued2017-10-05
dc.description.abstractFirearms 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.en_UK
dc.identifier.citationOdysseas Kechagias-Stamatis, Nabil Aouf, David Nam and Carole Belloni. Automatic x-ray image segmentation and clustering for threat detection. Target and Background Signatures III, 11-14 September 2017, Warsaw, Poland.en_UK
dc.identifier.issn0277-786X
dc.identifier.urihttps://doi.org/10.1117/12.2277190
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13502
dc.language.isoenen_UK
dc.publisherSPIEen_UK
dc.titleAutomatic x-ray image segmentation and clustering for threat detectionen_UK
dc.typeConference paperen_UK

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