{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-spectral-imaging-via-computed","title":"Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiation","arxiv_id":"1810.11823","date":"2018-10-28","proceeding":null,"authors":["Christian Kehl","Wail Mustafa","Jan Kehres","Anders Bjorholm Dahl","Ulrik Lund Olsen"],"abstract":"Multi-spectral computed tomography is an emerging technology for the\nnon-destructive identification of object materials and the study of their\nphysical properties. Applications of this technology can be found in various\nscientific and industrial contexts, such as luggage scanning at airports.\nMaterial distinction and its identification is challenging, even with spectral\nx-ray information, due to acquisition noise, tomographic reconstruction\nartefacts and scanning setup application constraints. We present MUSIC - and\nopen access multi-spectral CT dataset in 2D and 3D - to promote further\nresearch in the area of material identification. We demonstrate the value of\nthis dataset on the image analysis challenge of object segmentation purely\nbased on the spectral response of its composing materials. In this context, we\ncompare the segmentation accuracy of fast adaptive mean shift (FAMS) and\nunconstrained graph cuts on both datasets. We further discuss the impact of\nreconstruction artefacts and segmentation controls on the achievable results.\nDataset, related software packages and further documentation are made available\nto the imaging community in an open-access manner to promote further\ndata-driven research on the subject","url_abs":"http://arxiv.org/abs/1810.11823v1","url_pdf":"http://arxiv.org/pdf/1810.11823v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"music","name":"MUSIC","full_name":"Multi-Spectral Imaging via Computed Tomography"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}