Davide Ceneda

536 citations
21 papers · 397 · h-index 9

Impact in

Papers in

Davide Ceneda

19 papers receiving 391 citations

Peers

Davide Ceneda
Comparison fields: 5 of 64
  • Computer Vision and Pattern Recognition 301
  • Signal Processing 67
  • Information Systems and Management 34
  • Human-Computer Interaction 25
  • Artificial Intelligence 111
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Aoyu Wu Hong Kong
Charles D. Stolper United States
John Wenskovitch United States
Hannah Kim United States
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Tera Marie Green Canada
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Zening Qu United States
Daniel Seebacher Germany
Lauren Bradel United States
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Citations per year

Countries citing papers authored by Davide Ceneda

Since Specialization
Citations

This map shows the geographic impact of Davide Ceneda's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Davide Ceneda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davide Ceneda more than expected).

Fields of papers citing papers by Davide Ceneda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Davide Ceneda. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Davide Ceneda. The network helps show where Davide Ceneda may publish in the future.

Co-authors

The 19 scholars most cited alongside Davide Ceneda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Davide Ceneda Line = papers co-authored together Davide Ceneda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016188
2 201957
3 202024
4 202223
5 202219
6 202215
7 202114
8 201812
9 20199
10 20238
11 20207
12 20235
13 20243
14 20253
15 20163
16 20252
17 20242
18 20231
19 20241
20 20251

About Davide Ceneda

Davide Ceneda is a scholar working on Computer Vision and Pattern Recognition, Sociology and Political Science, Signal Processing, Computational Theory and Mathematics and Human-Computer Interaction, having authored 21 papers that have together received 397 indexed citations. Recurring topics across this work include Data Visualization and Analytics (17 papers), Video Analysis and Summarization (11 papers), Image and Video Quality Assessment (5 papers), Multimedia Communication and Technology (4 papers), Data Management and Algorithms (3 papers), Topological and Geometric Data Analysis (2 papers), Business Process Modeling and Analysis (1 paper) and Species Distribution and Climate Change (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (301 citations), Signal Processing (67 citations), Information Systems and Management (34 citations), Human-Computer Interaction (25 citations) and Artificial Intelligence (111 citations). Davide Ceneda has collaborated with scholars based in Austria, Germany and Netherlands. Frequent co-authors include Silvia Miksch, Theresia Gschwandtner, Marc Streit, Christian Tominski, Thorsten May, Hans‐Jörg Schulz, Mennatallah El‐Assady, Fabian Sperrle, Markus Wagner and Wolfgang Aigner. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, Visual Informatics, Computers & Graphics and Lecture notes in computer science.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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