This work addresses the question whether it is possible to design a computer-vision based automatic threat recognition (ATR) system so that it can adapt to changing specifications of a threat without having to create a new ATR each time. The changes in threat specifications, which may be warranted by intelligence reports and world events, are typically regarding the physical characteristics of what constitutes a threat: its material composition, its shape, its method of concealment, etc. Here we present our design of an AATR system (Adaptive ATR) that can adapt to changing specifications in materials characterization (meaning density, as measured by its x-ray attenuation coefficient), its mass, and its thickness. Our design uses a two-stage cascaded approach, in which the first stage is characterized by a high recall rate over the entire range of possibilities for the threat parameters that are allowed to change. The purpose of the second stage is to then fine-tune the performance of the overall system for the current threat specifications. The computational effort for this fine-tuning for achieving a desired PD PFA rate is far less than what it would take to create a new classifier with the same overall performance for the new set of threat specifications.
We present a survey of techniques for the reduction of streaking artefacts caused by met allic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-theart Met al Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of met al objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands. In dual energy computerized tomography (DECT) which is widely used in industrial areas and security inspection, met al artifact reduction (MAR) is a troublesome problem. Pronounced streaks appear in the atomic number reconstruction and the value appears to be highly inaccurate when met al objects are present. In this article, a practical MAR method for DECT is proposed. Firstly, sinogram segmentation based on active contour model is implemented to obtain the met al projection region (MPR). Then, TV inpainting for sinogram was applied before reconstruction. Experiments demonstrate that, with our MAR method, the accuracy and image quality of the atomic number can be greatly improved. In aviation security, checked luggage is screened by computed tomography scanning. Met al objects in the bags create artifacts that degrade image quality. Though there exist met al artifact reduction (MAR) methods mainly in medical imaging literature, they require knowledge of the materials in the scan, or are outlier rejection methods.To improve and evaluate a MAR method we previously introduced, that does not require knowledge of the materials in the scan, and gives good results on data with large quantities and different kinds of met al.We describe in detail an optimization which de-emphasizes met al projections and has a constraint for beam hardening and scatter. This method isolates and reduces artifacts in an intermediate image, which is then fed to a previously published sinogram replacement method. We evaluate the algorithm for luggage data containing multiple and large met al objects. We define measures of artifact reduction, and compare this method against others in MAR literature.Met al artifacts were reduced in our test images, even for multiple and large met al objects, without much loss of structure or resolution.Our MAR method outperforms the methods with which we compared it. Our approach does not make assumptions about image content, nor does it discard met al projections.