Tools
Tools
Bolton-King, RS 
ORCID: https://orcid.org/0000-0002-9208-7857, Bencsik, M ORCID: https://orcid.org/0000-0002-6278-0378, Evans, JPO ORCID: https://orcid.org/0000-0001-9831-1461, Smith, CL, Allsop, DF, Painter, JD and Cranton, WM,
2012.
Numerical classification of curvilinear structures for the identification of pistol barrels.
Forensic Science International, 220 (1-3), pp. 197-209.
ISSN 0379-0738
Preview |
Text
2365256_Bolton-King.pdf - Post-print Download (271kB) | Preview |
Abstract
This paper demonstrates a numerical pattern recognition method applied to curvilinear image structures. These structures are extracted from physical cross-sections of cast internal pistol barrel surfaces. Variations in structure arise from gun design and manufacturing method providing a basis for discrimination and identification.
Binarised curvilinear land transition images are processed with fast Fourier transform on which principal component analysis is performed. One-way analysis of variance (95 % confidence interval) concludes significant differentiation between 11 barrel manufacturers when calculating weighted Euclidean distance between any trio of land transitions and an average land transition for each barrel in the database. The proposed methodology is therefore a promising novel approach for the classification and identification of firearms.
| Item Type: | Journal article |
|---|---|
| Publication Title: | Forensic Science International |
| Creators: | Bolton-King, R.S., Bencsik, M., Evans, J.P.O., Smith, C.L., Allsop, D.F., Painter, J.D. and Cranton, W.M. |
| Publisher: | Elsevier BV |
| Date: | 10 July 2012 |
| Volume: | 220 |
| Number: | 1-3 |
| ISSN: | 0379-0738 |
| Identifiers: | Number Type 10.1016/j.forsciint.2012.03.002 DOI 2365256 Other |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Laura Borcherds |
| Date Added: | 07 Feb 2025 09:20 |
| Last Modified: | 07 Feb 2025 09:20 |
| URI: | https://irep.ntu.ac.uk/id/eprint/52982 |
Actions (login required)
 |
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year
