Bastan, Muhammet2025-10-242025-10-2420150932-80921432-1769https://doi.org/10.1007/s00138-015-0706-xhttps://hdl.handle.net/20.500.12899/3299Automatic 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.eninfo:eu-repo/semantics/closedAccessMulti-view object detection; Structured learning; Branch-and-bound search; Local features; X-ray imaging for security applicationsArticle10.1007/s00138-015-0706-x267-8104510602-s2.0-84943351314Q2WOS:000362576500013Q2