Uncertainty visualization in 3D scalar data

dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNot applicableen
dc.check.reasonNo embargo requireden
dc.check.typeNo Embargo Required
dc.contributor.advisorProvan, Gregoryen
dc.contributor.advisorMurphy, Daviden
dc.contributor.advisorÓ Mathúna, S. Cianen
dc.contributor.authorMa, Ji
dc.contributor.funderIrish Research Council for Science Engineering and Technologyen
dc.date.accessioned2014-04-16T13:49:22Z
dc.date.available2014-04-16T13:49:22Z
dc.date.issued2014
dc.date.submitted2014
dc.description.abstractOne problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.en
dc.description.sponsorshipThe Irish Research Council for Science Engineering and Technology (EMBARK)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMa, J. 2014. Uncertainty visualization in 3D scalar data. PhD Thesis, University College Cork.en
dc.identifier.urihttps://hdl.handle.net/10468/1535
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2014, Ji Maen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectUncertainty visualizationen
dc.subject3D scalar dataen
dc.subjectData visualizationen
dc.subjectVolume renderingen
dc.thesis.opt-outfalse
dc.titleUncertainty visualization in 3D scalar dataen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisorg.provan@cs.ucc.ie
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