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dc.contributor.authorGupta, Ankur
dc.date.accessioned2013-08-27T11:26:28Z
dc.date.available2013-08-27T11:26:28Z
dc.date.issued2013-08-27
dc.identifier.urihttp://hdl.handle.net/10222/36257
dc.descriptionAnnotations play a key role in explaining and elaborating 3D illustrations. They support users in identifying and establishing a visual link between different components within a 3D model. However, one major issue with annotating 3D illustrations is that there are no standard guidelines that clearly define which annotation type or style to use or is preferred by users in supporting learning and identifying objects at different zooming levels. Often, the decision of which style to use is influenced by size of the components being annotated and the overall look and feel (i.e., reduced occlusion and visual clutter) of the annotated view in display. In our research, we try to understand how effectively the three types of textual annotation labels (internal, external, and annotation boxes) can support users in learning, identifying, and navigating through 3D objects. We report the results of a study that evaluates the efficiency and accuracy in searching for components inside a 3D model, measures the impact on learning (in recalling the names of various components and their locations), and analyzes for user preferences in interacting with a 3D model on a mobile form factor at different zooming levels. Results of our study reflect that participants preferred external style annotations over internal and box style annotations, and that the participant’s performance (for both efficiency and accuracy) in searching for components inside a 3D model was highest with external style annotations. We also found that participants recalled more components when annotated with external styles annotations. Our findings suggest that of the three textual annotation styles considered in this study, external style annotation is the best annotation style to use in an annotated 3D model.en_US
dc.description.abstractAnnotations play a key role in explaining and elaborating 3D illustrations. They support users in identifying and establishing a visual link between different components within a 3D model. However, one major issue with annotating 3D illustrations is that there are no standard guidelines that clearly define which annotation type or style to use or is preferred by users in supporting learning and identifying objects at different zooming levels. Often, the decision of which style to use is influenced by size of the components being annotated and the overall look and feel (i.e., reduced occlusion and visual clutter) of the annotated view in display. In our research, we try to understand how effectively the three types of textual annotation labels (internal, external, and annotation boxes) can support users in learning, identifying, and navigating through 3D objects. We report the results of a study that evaluates the efficiency and accuracy in searching for components inside a 3D model, measures the impact on learning (in recalling the names of various components and their locations), and analyzes for user preferences in interacting with a 3D model on a mobile form factor at different zooming levels. Results of our study reflect that participants preferred external style annotations over internal and box style annotations, and that the participant’s performance (for both efficiency and accuracy) in searching for components inside a 3D model was highest with external style annotations. We also found that participants recalled more components when annotated with external styles annotations. Our findings suggest that of the three textual annotation styles considered in this study, external style annotation is the best annotation style to use in an annotated 3D model.en_US
dc.language.isoen_USen_US
dc.subjectBoeingen_US
dc.subject3D Modelsen_US
dc.titleEvaluating Textual Annotations with Interactive 3D Modelsen_US
dc.typeThesisen_US
dc.date.defence2013-03-03
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. Dirk Arnolden_US
dc.contributor.thesis-readerDr. James Blusteinen_US
dc.contributor.thesis-readerDr. Stephen Brooksen_US
dc.contributor.thesis-supervisorDr. Kirstie Hawkeyen_US
dc.contributor.ethics-approvalReceiveden_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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