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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