Brian Isaac Medina brian.k.isaac-medina@durham.ac.uk
Postdoctoral Research Associate
Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery
Isaac-Medina, B.K.S.; Bhowmik, N.; Willcocks, C.G.; Breckon, T.P.
Authors
Dr Neelanjan Bhowmik neelanjan.bhowmik@durham.ac.uk
Post Doctoral Research Associate
Dr Chris Willcocks christopher.g.willcocks@durham.ac.uk
Associate Professor
Professor Toby Breckon toby.breckon@durham.ac.uk
Professor
Abstract
Dual-energy X-ray scanners are used for aviation security screening given their capability to discriminate materials inside passenger baggage. To facilitate manual operator inspection, a pseudo-colouring is assigned to the effective composition of the material. Recently, paired image to image translation models based on conditional Generative Adversarial Networks (cGAN) have shown to be effective for image colourisation. In this work, we investigate the use of such a model to translate from the raw X-ray energy responses (high, low, effective-Z) to the pseudo-coloured images and vice versa. Specifically, given N X-ray modalities, we train a cGAN conditioned in N − m domains to generate the remaining m representation. Our method achieves a mean squared error (MSE) of 16.5 and a structural similarity index (SSIM) of 0.9815 when using the raw modalities to generate the pseudo-colour representation. Additionally, raw X-ray high energy, low energy and effective-Z projections were generated given the pseudo-colour image with minimum MSE of 2.57, 5.63 and 1.43, and maximum SSIM of 0.9953, 0.9901 and 0.9921. Furthermore, we assess the quality of our synthesised pseudo-colour reconstructions by measuring the performance of two object detection models originally trained on real X-ray pseudo-colour images over our generated pseudo-colour images. Interestingly, our generated pseudo-colour images obtain marginally improved detection performance than the corresponding real X-ray pseudo-colour images, showing that meaningful representations are synthesized and that these reconstructions are applicable for differing aviation security tasks.
Citation
Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022, June). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Start Date | Jun 19, 2022 |
End Date | Jun 24, 2022 |
Acceptance Date | Apr 11, 2022 |
Online Publication Date | Jun 18, 2022 |
Publication Date | 2022-06 |
Deposit Date | May 4, 2022 |
Publicly Available Date | Jun 25, 2022 |
Publisher | Institute of Electrical and Electronics Engineers |
ISBN | 9781665487405 |
DOI | https://doi.org/10.1109/cvprw56347.2022.00048 |
Public URL | https://durham-repository.worktribe.com/output/1136610 |
Files
Accepted Conference Proceeding
(7.4 Mb)
PDF
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening
(2023)
Presentation / Conference Contribution
Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
(2023)
Presentation / Conference Contribution
Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
(2023)
Presentation / Conference Contribution
UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery
(2022)
Presentation / Conference Contribution
Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
(2021)
Presentation / Conference Contribution
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