Uncertainty visualization in 3D scalar data

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dc.contributor.advisor Provan, Gregory en
dc.contributor.advisor Murphy, David en
dc.contributor.advisor Ó Mathúna, S. Cian en
dc.contributor.author Ma, Ji
dc.date.accessioned 2014-04-16T13:49:22Z
dc.date.available 2014-04-16T13:49:22Z
dc.date.issued 2014
dc.date.submitted 2014
dc.identifier.citation Ma, J. 2014. Uncertainty visualization in 3D scalar data. PhD Thesis, University College Cork. en
dc.identifier.uri http://hdl.handle.net/10468/1535
dc.description.abstract One 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.sponsorship The Irish Research Council for Science Engineering and Technology (EMBARK) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2014, Ji Ma en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Uncertainty visualization en
dc.subject 3D scalar data en
dc.subject Data visualization en
dc.subject Volume rendering en
dc.title Uncertainty visualization in 3D scalar data en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD (Science) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Irish Research Council for Science Engineering and Technology en
dc.description.status Not peer reviewed en
dc.internal.school Computer Science en
dc.internal.school Tyndall National Institute en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor g.provan@cs.ucc.ie
dc.internal.conferring Summer Conferring 2014


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