Heike Leitte

35 papers receiving 521 citations

Peers

Heike Leitte
Comparison fields: 5 of 87
  • Computer Graphics and Computer-Aided Design 55
  • Computer Vision and Pattern Recognition 276
  • Biophysics 74
  • Computational Theory and Mathematics 156
  • Signal Processing 43
Replace Július Parulek with:
Július Parulek Norway
Dan Maljovec United States
Elizabeth Munch United States
E. Wes Bethel United States
Jarkko Venna Finland
Primož Škraba United States
Jerzy W. Jaromczyk United States
Paulo Rauber Netherlands
Keyan Ding China
Erin Wolf Chambers United States
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Citations per field
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Citations per year

Countries citing papers authored by Heike Leitte

Since Specialization
Citations

This map shows the geographic impact of Heike Leitte'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 Heike Leitte with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heike Leitte more than expected).

Fields of papers citing papers by Heike Leitte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Heike Leitte. 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 Heike Leitte. The network helps show where Heike Leitte may publish in the future.

Co-authors

The 25 scholars most cited alongside Heike Leitte, 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 Heike Leitte Line = papers co-authored together Heike Leitte links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016111
2 201685
3 201240
4 201534
5 201733
6 201731
7 201228
8 201726
9 202117
10 201915
11 201914
12 201613
13 201812
14 20148
15 20158
16 20207
17 20237
18 20227
19
From Theory to Usage: Requirements for successful Visualizations in Applications
20166
20 20155

About Heike Leitte

Heike Leitte is a scholar working on Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Artificial Intelligence, Computer Graphics and Computer-Aided Design and Control and Systems Engineering, having authored 38 papers that have together received 539 indexed citations. Recurring topics across this work include Data Visualization and Analytics (16 papers), Topological and Geometric Data Analysis (12 papers), Advanced Vision and Imaging (6 papers), Computer Graphics and Visualization Techniques (5 papers), Industrial Vision Systems and Defect Detection (3 papers), Data Analysis with R (3 papers), Manufacturing Process and Optimization (3 papers) and Advanced Neuroimaging Techniques and Applications (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (55 citations), Computer Vision and Pattern Recognition (276 citations), Biophysics (74 citations), Computational Theory and Mathematics (156 citations) and Signal Processing (43 citations). Heike Leitte has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Bastian Rieck, Christoph Garth, Jonas Lukasczyk, Alexander Schmitz, Alexis Maizel, Daniel von Wangenheim, Ernst H. K. Stelzer, Richard S. Smith, Ross Maciejewski and Federico Iuricich. Their work appears in journals such as Computer Graphics Forum, IEEE Transactions on Visualization and Computer Graphics, Computers & Chemical Engineering, Current Biology and Journal of Imaging Science and Technology.

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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