Demo Data from EDBT 2026 / reviewers in the wild / expert
Anna Schütz
dblp:273/7321
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0001-8289-5466ORCID · reported
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 |
Visualization and visual analytics · 44% Multimedia analysis and retrieval · 44% Image and video processing · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › image analysis
image comparison |
0.4 | 1 | 2020 | VeCHArt: Visually Enhanced Comparison of Historic Art Using an Automated Line-Based Synchronization Technique · IEEE Trans. Vis. Comput. Graph. 2020 |
Visualization and visual analytics
visual analytics |
0.4 | 1 | 2020 | VeCHArt: Visually Enhanced Comparison of Historic Art Using an Automated Line-Based Synchronization Technique · IEEE Trans. Vis. Comput. Graph. 2020 |
Methods — techniques the papers use, named apart from their topics
stroke-pattern matching · 0.4image alignment · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | VeCHArt: Visually Enhanced Comparison of Historic Art Using an Automated Line-Based Synchronization TechniqueabstractThe analysis of subtle deviations between different versions of historical prints has been a long-standing challenge in art history research. So far, this challenge has required extensive domain knowledge, fine-tuned expert perception, and time-consuming manual labor. In this paper we introduce an explorative visual approach to facilitate fast and accurate support for the task of comparing differences between prints such as engravings and woodcuts. To this end, we have developed a customized algorithm that detects similar stroke-patterns in prints and matches them in order to allow visual alignment and automated deviation highlighting. Our visual analytics system enables art history researchers to quickly detect, document, and categorize qualitative and quantitative discrepancies, and to analyze these discrepancies using comprehensive interactions. To evaluate our approach, we conducted a user study involving both experts on historical prints and laypeople. Using our new interactive technique, our subjects found about 20 percent more differences compared to regular image viewing software as well as "paper-based" comparison. Moreover, the laypeople found the same differences as the experts when they used our system, which was not the case for conventional methods. Informal feedback showed that both laypeople and experts strongly preferred employing our system to working with conventional methods. Hermann Pflüger, Dennis Thom, Anna Schütz, Daniela Bohde, Thomas Ertl |
IEEE Trans. Vis. Comput. Graph. | 3 |