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• W4360764549 abstract "X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items, using 2D X-ray imagery is of primary interest in recent years. We address this task by introducing joint object sub-component level segmentation and classification strategy using deep Convolution Neural Network architecture. The performance is evaluated over a dataset of cluttered X-ray baggage security imagery, consisting of consumer electrical and electronics items using variants of dual-energy X-ray imagery (pseudo-colour, high, low, and effective-Z). The proposed joint sub-component level segmentation and classification approach achieve ∼99% true positive and ∼5% false positive for anomaly detection task." @default.
• W4360764549 created "2023-03-25" @default.
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• W4360764549 date "2022-12-01" @default.
• W4360764549 modified "2023-10-16" @default.
• W4360764549 title "Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery" @default.
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• W4360764549 doi "https://doi.org/10.1109/icmla55696.2022.00230" @default.
• W4360764549 hasPublicationYear "2022" @default.
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