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Proceedings Paper

Representation-learning for anomaly detection in complex x-ray cargo imagery
Author(s): Jerone T. A. Andrews; Nicolas Jaccard; Thomas W. Rogers; Lewis D. Griffin
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Paper Abstract

Existing approaches to automated security image analysis focus on the detection of particular classes of threat. However, this mode of inspection is ineffectual when dealing with mature classes of threat, for which adversaries have refined effective concealment techniques. Furthermore, these methods may be unable to detect potential threats that have never been seen before. Therefore, in this paper, we investigate an anomaly detection framework, at X-ray image patch-level, based on: (i) image representations, and (ii) the detection of anomalies relative to those representations. We present encouraging preliminary results, using representations learnt using convolutional neural networks, as well as several contributions to a general-purpose anomaly detection algorithm based on decision-tree learning.

Paper Details

Date Published: 1 May 2017
PDF: 11 pages
Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870E (1 May 2017); doi: 10.1117/12.2261101
Show Author Affiliations
Jerone T. A. Andrews, Univ. College London (United Kingdom)
Nicolas Jaccard, Univ. College London (United Kingdom)
Thomas W. Rogers, Univ. College London (United Kingdom)
Lewis D. Griffin, Univ. College London (United Kingdom)

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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