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DC poleHodnotaJazyk
dc.contributor.authorGurijala, Krishna
dc.contributor.authorWang, Lei
dc.contributor.authorKaufman, Arie
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-17T14:32:35Z
dc.date.available2023-10-17T14:32:35Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 117-126.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54417
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectdifuzecs
dc.subjecttvarová analýzacs
dc.subjectMonte Carlocs
dc.subjectdetekce rakoviny tlustého střevacs
dc.subjectfunkce přenosucs
dc.titleMonte Carlo Based Real-Time Shape Analysis in Volumesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWe introduce a Monte Carlo based real-time diffusion process for shape-based analysis in volumetric data. The diffusion process is carried out by using tiny massless particles termed shapetons, which are used to capture the shape information. Initially, these shapetons are randomly distributed inside the voxels of the volume data. The shapetons are then diffused in a Monte Carlo fashion to obtain the shape information. The direction of propagation for the shapetons is monitored by the Volume Gradient Operator (VGO). This operator is known for successfully capturing the shape information and thus the shape information is well captured by the shapeton diffusion method. All the shapetons are diffused simultaneously and all the results can be monitored in real-time. We demonstrate several important applications of our approach including colon cancer detection and design of shape-based transfer functions. We also present supporting results for the applications and show that this method works well for volumes. We show that our approach can robustly extract shape-based features and thus forms the basis for improved classification and exploration of features based on shape.en
dc.subject.translatedShapeton diffusionen
dc.subject.translatedshape analysisen
dc.subject.translatedMonte Carloen
dc.subject.translatedcolon cancer detectionen
dc.subject.translatedtransfer-functionen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.15
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2023: Full Papers Proceedings

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