This study presents an innovative method far from linear density and effective atomic number (Zeff) based coloring methods in X-Ray Baggage Inspection Systems. Instead of displaying a standard X-Ray image, it is recommended to display several images obtained by parametric modifications in succession called Progressive Sequential Imaging (PSI). The animated images produced by the proposed PSI enable the scanned packages to be examined much shorter than the traditional manual scanning approach through various color or gray-scale image models. Besides, details of the objects, low penetration regions, and materials behind metallic plates are observed more clearly. PSI provides a more powerful and newer imaging method for the X-Ray baggage scanner.
A single-point energy source in X-Ray imaging devices has an artifact of energy loss due to the inverse square rule that also affects the penetration ability. The difference between low and Hi responses is the key feature for effective atomic number estimation in dual-energy X-Ray devices. As the penetration capability alters depending on the location of objects in the devices, the reliability of Zeff estimates drops. Another reason behind this is the difference in the responses of low and Hi detectors, where Hi read-outs drop faster than Lo read-outs. Thus, the log ratio/Zeff polynomial modeling estimates lower effective atomic numbers for the same objects as they get further from the generator. This study proposes a novel approach to handle position dependency on atomic number estimation supported by detailed analyses of material type and distance. We model the behavior of different metal/inorganic/organic materials according to their locations in the X-Ray conveyor band and implement a correction mapping approach to preserve the consistency of estimates. Consistency of Zeff is very important, especially for threat detection, where threat regions are defined as limited Zeff intervals for dual energy devices. The proposed approach enables material classification consistency against objects’ position on the conveyor band and provides a reliable infrastructure for alarm-based applications in X-Ray devices. The in-depth experiments for various objects, including inert explosives, organic materials, and metallic objects, show the robustness of the proposed band correction approach.
In this study, an efficient end-to-end material classification is proposed for dual energy x-ray imaging devices. Performing prompt geometric and radiometric calibrations, we exploit polynomial modeling on low-high energy ratios to estimate effective atomic numbers (EAN) of the objects, that is based and experimented over twentyfive different materials. Special attention is devoted for dense materials on which the ratio polynomial modeling performs poorly as the thickness increases. A novel material peeling approach is also proposed that uncovers blocked or encapsulated objects and enable precise EAN estimation in cluttered images. The proposed approach provides visually informative x-ray image segmentation.
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