Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 98964
- Type
- D - Journal article
- DOI
-
10.1016/j.patcog.2015.01.010
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 1961
- Volume
- 48
- Issue
- 6
- ISSN
- 00313203
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
https://doi.org/10.1016/j.patcog.2015.01.010
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
A - Innovative Computing
- Citation count
- 24
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Within the application of object segmentation to volumetric 3D CT baggage security imagery, this was the first published approach for object segmentation based on DECT information operating on such cluttered, noisy imagery. Led to invited presentation by Breckon at US Government ADSA workshop (2016 - http://www.northeastern.edu/alert/transitioning-technology/adsa/adsa14/) and Breckon received grants from both the UK/US government to extend this work (1: US DHS Contract: HSHQDC-16-A-B008/HSHQDC-16-J-00287 – Sub-contract: 505118-78052 / 2: UK DfT FASS Project: ACC6007893). Statistical evaluation is performed against the largest CT baggage specific dataset ever constructed (supplied onwards to UK government).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -