SD&A 2020: Evaluating the stereoscopic display of visual entropy glyphs in complex environments
Channel: IS&T Electronic Imaging (EI) Symposium
Date: 2020-07-22

This presentation was delivered at the 31st annual Stereoscopic Displays and Applications conference (27-29 January 2020) held in Burlingame, CA USA. For more information see: http://stereoscopic.org 

Title: Evaluating the stereoscopic display of visual entropy glyphs in complex environments (SD&A-243)

Speaker: Nicolas Holliman, University of Newcastle (UK)

Abstract: A key challenge in data visualization is predicting when a visualization will work. In most visualizations this relates to ensuring the signal in the data is visually searchable and that users can accurately and quickly detect the signal among what is otherwise noise for the task in hand. The evidence for the benefits of stereoscopic presentation of data in 3D environments is mixed. We previously proposed a set of visual entropy glyphs for representing data pairs (data value, variance measure) that have potential for a wide range of uses where data values and uncertainty about data values must be represented. We previously tested the visual entropy glyphs in a visual search task on monoscopic displays and here we will evaluate the same glyphs in a complex environment when displayed stereoscopically. The environmental data we visualize is from sensors measured as a mean value, such as temperature, with a variance calculated over a unit of time, for example one hour, using the visual entropy glyphs within a 3D city model. Our hypothesis is that there is a performance advantage from using stereoscopic displays when searching for visual entropy glyphs in a pre-cued visual search task compared to the same task undertaken on a monoscopic display. We will also present correlations between the experimental results and extant visual metrics related to predicting task performance including edge congestion and visual salience. Our aim is to understand if we can predict stereoscopic visualization performance using automated metrics.

Biography: Nicolas Holliman is Professor of Visualization in the School of Computing at Newcastle University. At Newcastle he is a member of the Digital Institute and is also part of the team working to establish the new National Innovation Centre for Data. He is a graduate in Computing with Electronics from the Departments of Computer Science and Applied Physics at Durham University, his PhD from Leeds University investigated novel parallel computing architectures for high performance graphics and was sponsored by IBM UK. His research has always involved multi-disciplinary or multi-perspective collaborations.

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