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Yuanyang Zhong

dblp:352/5674 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0002-0548-7423ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 77% Image and video processing · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › information visualization › statistical graphics
categorical data visualization
0.812024
Design Characterization for Black-and-White Textures in Visualization · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › visualization evaluation
perceptual evaluation
0.812024
Design Characterization for Black-and-White Textures in Visualization · IEEE Trans. Vis. Comput. Graph. 2024
Image and video processing › texture analysis › texture modeling
texture design
0.812024
Design Characterization for Black-and-White Textures in Visualization · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics
visual encoding
0.812024
Design Characterization for Black-and-White Textures in Visualization · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › visualization design
chart design
0.212024
Design Characterization for Black-and-White Textures in Visualization · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

perceptual assessment · 0.8controlled experiment · 0.8
YearPublicationVenuePosition
2024 Design Characterization for Black-and-White Textures in Visualization
abstract
We investigate the use of 2D black-and-white textures for the visualization of categorical data and contribute a summary of texture attributes, and the results of three experiments that elicited design strategies as well as aesthetic and effectiveness measures. Black-and-white textures are useful, for instance, as a visual channel for categorical data on low-color displays, in 2D/3D print, to achieve the aesthetic of historic visualizations, or to retain the color hue channel for other visual mappings. We specifically study how to use what we call geometric and iconic textures. Geometric textures use patterns of repeated abstract geometric shapes, while iconic textures use repeated icons that may stand for data categories. We parameterized both types of textures and developed a tool for designers to create textures on simple charts by adjusting texture parameters. 30 visualization experts used our tool and designed 66 textured bar charts, pie charts, and maps. We then had 150 participants rate these designs for aesthetics. Finally, with the top-rated geometric and iconic textures, our perceptual assessment experiment with 150 participants revealed that textured charts perform about equally well as non-textured charts, and that there are some differences depending on the type of chart.
Tingying He, Yuanyang Zhong, Petra Isenberg, Tobias Isenberg 0001
IEEE Trans. Vis. Comput. Graph.2