Title
A learning-based approach to explosives detection using Multi-Energy X-Ray Computed Tomography
Abstract
In this paper we consider the task of classifying materials into explosives and non-explosives according to features obtainable from Multi-Energy X-ray Computed Tomography (MECT) measurements. The discriminative ability of MECT derives from its sensitivity to the attenuation versus energy curves of materials. Thus we focus on the fundamental information available in these curves and features extracted from them. We study the dimensionality and span of these curves for a set of explosive and non-explosive compounds and show that their space is larger than two-dimensional, as is typically assumed. In addition, we build support vector machine classifiers with different feature sets and find superior classification performance when using more than two features and when using features different than the standard photoelectric and Compton coefficients. These results suggest the potential for improved detection performance relative to conventional dual-energy X-ray systems.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5946904
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
computerised tomography,explosives,feature extraction,image classification,learning (artificial intelligence),support vector machines,MECT,SVM classifier,dual-energy X-ray systems,energy curves,explosives detection,feature extraction,learning,multienergy X-ray computed tomography,non-explosive compounds,support vector machine,Classification,Dimensionality reduction,Multi-Energy X-ray tomography,National security,Support vector machines
Dimensionality reduction,Pattern recognition,Computer science,Support vector machine,Explosive material,Feature extraction,Curse of dimensionality,Artificial intelligence,Attenuation,Contextual image classification,Discriminative model
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4577-0537-3
978-1-4577-0537-3
1
PageRank 
References 
Authors
0.63
2
5
Name
Order
Citations
PageRank
Limor Eger110.96
Synho Do29412.86
Prakash Ishwar395167.13
W. Clem Karl422435.45
Homer Pien5446.48