Quanjie Zhang

dblp:284/4827 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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 · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › clustering
hierarchical clustering
0.512021
SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects · IEEE Trans. Vis. Comput. Graph. 2021
Visualization and visual analytics › time series visualization
streamgraph
0.512021
SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects · IEEE Trans. Vis. Comput. Graph. 2021

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

hierarchical clustering · 0.5gaussian weighting · 0.5
YearPublicationVenuePosition
2021 SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects
abstract
In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptual foundation for their design. As the geometry of a streamgraph is controlled by its baseline (the bottom-most curve) and the ordering of the layers, we re-interpret baseline computation and layer ordering algorithms in terms of reducing sine illusion effects. For baseline computation, we improve previous methods by introducing a Gaussian weight to penalize layers with large thickness changes. For layer ordering, three design requirements are proposed and implemented through a hierarchical clustering algorithm. Quantitative experiments and user studies demonstrate that SineStream improves the readability and aesthetics of streamgraphs compared to state-of-the-art methods.
Chuan Bu, Quanjie Zhang, Qianwen Wang 0001, Jian Zhang 0070, Michael Sedlmair, Oliver Deussen, Yunhai Wang
IEEE Trans. Vis. Comput. Graph.2