A Robust Approach to Automatically Locating Grooves in 3D Bullet Land Scans

dc.contributor.author Rice, Kiegan
dc.contributor.author Genschel, Ulrike
dc.contributor.author Hofmann, Heike
dc.contributor.department Center for Statistics and Applications in Forensic Evidence
dc.contributor.department Statistics
dc.date 2020-04-10T01:12:10.000
dc.date.accessioned 2020-06-30T01:58:13Z
dc.date.available 2020-06-30T01:58:13Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-12-30
dc.description.abstract <p>Land engraved areas (LEAs) provide evidence to address the same source–different source problem in forensic firearms examination. Collecting 3D images of bullet LEAs requires capturing portions of the neighboring groove engraved areas (GEAs). Analyzing LEA and GEA data separately is imperative to accuracy in automated comparison methods such as the one developed by Hare et al. (Ann Appl Stat 2017;11, 2332). Existing standard statistical modeling techniques often fail to adequately separate LEA and GEA data due to the atypical structure of 3D bullet data. We developed a method for automated removal of GEA data based on robust locally weighted regression (LOESS). This automated method was tested on high‐resolution 3D scans of LEAs from two bullet test sets with a total of 622 LEA scans. Our robust LOESS method outperforms a previously proposed “rollapply” method. We conclude that our method is a major improvement upon rollapply, but that further validation needs to be conducted before the method can be applied in a fully automated fashion.</p>
dc.description.comments <p>This is a manuscript of an article published as Rice, Kiegan, Ulrike Genschel, and Heike Hofmann. "A Robust Approach to Automatically Locating Grooves in 3D Bullet Land Scans." <em>Journal of Forensic Sciences</em> (2019). Posted with permission of CSAFE.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/csafe_pubs/26/
dc.identifier.articleid 1033
dc.identifier.contextkey 17326913
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath csafe_pubs/26
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/20373
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/csafe_pubs/26/Rice__2019__Journal_of_Forensic_Sciences__Manuscript.pdf|||Fri Jan 14 23:01:42 UTC 2022
dc.source.uri 10.1111/1556-4029.14263
dc.subject.disciplines Forensic Science and Technology
dc.title A Robust Approach to Automatically Locating Grooves in 3D Bullet Land Scans
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication d8a3c72b-850f-40f6-87c4-8812547080c7
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
File
Original bundle
Now showing 1 - 1 of 1
Name:
Rice__2019__Journal_of_Forensic_Sciences__Manuscript.pdf
Size:
1.94 MB
Format:
Adobe Portable Document Format
Description:
Collections