Object Recognition in Multi-View Dual Energy X-ray Images

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dc.contributor.authorBastan, Muhammet
dc.contributor.authorByeon, Wonmin
dc.contributor.authorBreuel, Thomas M.
dc.date.accessioned2025-10-24T18:10:09Z
dc.date.available2025-10-24T18:10:09Z
dc.date.issued2013
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description24th British Machine Vision Conference -- SEP 09-13, 2013 -- Bristol, ENGLAND
dc.description.abstractObject recognition in X-ray images is an interesting application of machine vision that can help reduce the workload of human operators of X-ray scanners at security checkpoints. In this paper, we first present a comprehensive evaluation of image classification and object detection in X-ray images using standard local features in a BoW framework with (structural) SVMs. Then, we extend the features to utilize the extra information available in dual energy X-ray images. Finally, we propose a multi-view branch-and-bound algorithm for multi-view object detection. Through extensive experiments on three object categories, we show that the classification and detection performance substantially improves with the extended features and multiple views.
dc.description.sponsorshipQualcomm,Dyson,Microsoft Res,Inst Engn Technol Journals,HP
dc.identifier.doi10.5244/C.27.130
dc.identifier.issn#DEĞER!
dc.identifier.urihttps://doi.org/10.5244/C.27.130
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3998
dc.identifier.wosWOS:000346352700127
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherB M V A Press
dc.relation.ispartofProceedings Of The British Machine Vision Conference 2013
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20251023
dc.subject[No Keywords]
dc.titleObject Recognition in Multi-View Dual Energy X-ray Images
dc.typeConference Object

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