Demo Data from EDBT 2026 / reviewers in the wild / expert
Connie Wan
dblp:150/1380
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Visual content generation and editing · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visual content generation and editing
example-based synthesis |
0.2 | 1 | 2014 | DecoBrush: drawing structured decorative patterns by example · ACM Trans. Graph. 2014 |
Methods — techniques the papers use, named apart from their topics
user-guided refinement · 0.2data-driven synthesis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | DecoBrush: drawing structured decorative patterns by exampleabstractStructured decorative patterns are common ornamentations in a variety of media like books, web pages, greeting cards and interior design. Creating such art from scratch using conventional software is time consuming for experts and daunting for novices. We introduce DecoBrush, a data-driven drawing system that generalizes the conventional digital "painting" concept beyond the scope of natural media to allow synthesis of structured decorative patterns following user-sketched paths. The user simply selects an example library and draws the overall shape of a pattern. DecoBrush then synthesizes a shape in the style of the exemplars but roughly matching the overall shape. If the designer wishes to alter the result, DecoBrush also supports user-guided refinement via simple drawing and erasing tools. For a variety of example styles, we demonstrate high-quality user-constrained synthesized patterns that visually resemble the exemplars while exhibiting plausible structural variations. Jingwan Lu, Connelly Barnes, Connie Wan, Paul Asente, Radomír Mech, Adam Finkelstein |
ACM Trans. Graph. | 3 |