A learning-based approach to explosives detection using Multi-Energy X-Ray Computed Tomography
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.