@inproceedings{6c27e45949e84b37bb2b4c309acf00a1,
title = "Detecting Prohibited Items in X-Ray Images: A Contour Proposal Learning Approach",
abstract = "X-ray baggage screening plays a vital role in aviation security. Manual inspection of potentially anomalous items is challenging due to the clutter and occlusion within Xray scans. Here, we address this issue by presenting an object-boundaries driven framework for the automated detection of suspicious items from X-ray baggage scans. Rather than recognizing objects directly from the X-ray images, our two-stage detection approach first extracts contour-based proposals using a novel cascaded structure tensor technique and subsequently passes the candidate proposals to a single feed-forward convolutional neural network for recognition. Thorough experimentation on GDXray and SIXray datasets demonstrates that the proposed model achieves a mean area under the curve of 0.9878, outperforming the existing renown state-of-the-art object detection frameworks.",
keywords = "Baggage screening, Convolution Neural Network, Image analysis, Structure Tensor, X-ray images",
author = "Taimur Hassan and Meriem Bettayeb and Samet Akcay and Salman Khan and Mohammed Bennamoun and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference date: 25-09-2020 Through 28-09-2020",
year = "2020",
month = oct,
doi = "10.1109/ICIP40778.2020.9190711",
language = "British English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2016--2020",
booktitle = "2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings",
address = "United States",
}