Multi-view object detection in dual-energy X-ray images

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dc.contributor.authorBastan, Muhammet
dc.date.accessioned2025-10-24T18:08:47Z
dc.date.available2025-10-24T18:08:47Z
dc.date.issued2015
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractAutomatic inspection of X-ray scans at security checkpoints can improve the public security. X-ray images are different from photographic images. They are transparent. They contain much less texture. They may be highly cluttered. Objects may undergo in- and out-of-plane rotations. On the other hand, scale and illumination change is less of an issue. More importantly, X-ray imaging provides extra information which are usually not available in regular images: dual-energy imaging, which provides material information about the objects; and multi-view imaging, which provides multiple images of objects from different viewing angles. Such peculiarities of X-ray images should be leveraged for high-performance object recognition systems to be deployed on X-ray scanners. To this end, we first present an extensive evaluation of standard local features for object detection on a large X-ray image dataset in a structured learning framework. Then, we propose two dense sampling methods as keypoint detector for textureless objects and extend the SPIN color descriptor to utilize the material information. Finally, we propose a multi-view branch-and-bound search algorithm for multi-view object detection. Through extensive experiments on three object categories, we show that object detection performance on X-ray images improves substantially with the help of extended features and multiple views.
dc.description.sponsorshipBundesministerium fur Bildung und Forschung of Germany [FKZ 13N11125]
dc.description.sponsorshipThe major part of this work was done when the author was a post-doctoral researcher at the Image Understanding and Pattern Recognition Group (IUPR) of Technical University of Kaiserslautern, Germany; as part of the SICURA project, which was supported by the Bundesministerium fur Bildung und Forschung of Germany with ID FKZ 13N11125 (2010-2013). The X-ray data were recorded for the SICURA project in collaboration with Smiths-Heimann (http://www.smithsdetection.com) a manufacturer of X-ray machines and one of the partners in the SICURA project. We are thankful to the project partners and members of the IUPR research group.
dc.identifier.doi10.1007/s00138-015-0706-x
dc.identifier.endpage1060
dc.identifier.issn0932-8092
dc.identifier.issn1432-1769
dc.identifier.issue7-8
dc.identifier.scopus2-s2.0-84943351314
dc.identifier.scopusqualityQ2
dc.identifier.startpage1045
dc.identifier.urihttps://doi.org/10.1007/s00138-015-0706-x
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3299
dc.identifier.volume26
dc.identifier.wosWOS:000362576500013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMachine Vision And Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20251023
dc.subjectMulti-view object detection; Structured learning; Branch-and-bound search; Local features; X-ray imaging for security applications
dc.titleMulti-view object detection in dual-energy X-ray images
dc.typeArticle

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