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dc.contributor.authorZhang, Yuchong
dc.contributor.authorMa, Yong
dc.contributor.authorAdel, Omrani
dc.contributor.authorYadav, Rahul
dc.contributor.authorFjeld, Morten
dc.contributor.authorFratarcangeli, Marco
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-27T11:43:44Z
dc.date.available2020-07-27T11:43:44Z
dc.date.issued2020
dc.identifier.citationWSCG 2020: full papers proceedings: 28th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 126-136.en
dc.identifier.isbn978-80-86943-35-0
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-CSRN-3001.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38459
dc.format11 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2020: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectsegmentace obrazucs
dc.subjectOtsucs
dc.subjectk-znamenács
dc.subjectmikrovlnná tomografiecs
dc.titleAutomated Microwave Tomography (MWT) Image Segmentation: State-of-the-Art Implementation and Evaluationen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedInspired by the high performance in image-based medical analysis, this paper explores the use of advanced segmentation techniques for industrial Microwave Tomography (MWT). Our context is the visual analysis of moisture levels in porous foams undergoing microwave drying. We propose an automatic segmentation technique—MWT Segmentation based on K-means (MWTS-KM) and demonstrate its efficiency and accuracy for industrial use. MWTS-KM consists of three stages: image augmentation, grayscale conversion, and K-means implementation. To estimate the performance of this technique, we empirically benchmark its efficiency and accuracy against two well-established alternatives: Otsu and K-means. To elicit performance data, three metrics (Jaccard index, Dice coefficient and false positive) are used. Our results indicate that MWTS-KM outperforms the well-established Otsu and K-means, both in visually observable and objectively quantitative evaluation.en
dc.subject.translatedimage segmentationen
dc.subject.translatedOtsuen
dc.subject.translatedK-meansen
dc.subject.translatedmicrowave tomographyen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2020.3001.15
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2020: Full Papers Proceedings

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