A. Mouton
A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening
Mouton, A.; Breckon, T.P.
Abstract
Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent security regulations have led to a growing demand for more advanced imaging technologies. The role of X-ray Computed Tomography is thus rapidly expanding beyond the traditional materials-based detection of explosives. The development of computer vision and image processing techniques for the automated understanding of 3D baggage-CT imagery is however, complicated by poor image resolutions, image clutter and high levels of noise and artefacts. We discuss the recent and most pertinent advancements and identify topics for future research within the challenging domain of automated image understanding for baggage security screening CT.
Citation
Mouton, A., & Breckon, T. (2015). A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 23(5), 531-555. https://doi.org/10.3233/xst-150508
| Journal Article Type | Article |
|---|---|
| Acceptance Date | Jul 13, 2015 |
| Publication Date | Sep 21, 2015 |
| Deposit Date | Oct 4, 2015 |
| Publicly Available Date | Oct 5, 2015 |
| Journal | Journal of X-Ray Science and Technology |
| Print ISSN | 0895-3996 |
| Electronic ISSN | 1095-9114 |
| Publisher | IOS Press |
| Peer Reviewed | Peer Reviewed |
| Volume | 23 |
| Issue | 5 |
| Pages | 531-555 |
| DOI | https://doi.org/10.3233/xst-150508 |
| Keywords | Baggage screening, Automated image understanding, Dual-energy, Computed tomography, Computer vision. |
| Related Public URLs | http://community.dur.ac.uk/toby.breckon/publications/papers/mouton15review.pdf |
Files
Accepted Journal Article
(1.5 Mb)
PDF
You might also like
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
(2023)
Conference Proceeding
ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction
(2023)
Conference Proceeding
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
(2023)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search