Julia Neumann
Impact in
- Signal Processing top 10%
- Data Management and Algorithms
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- Face and Expression Recognition
- Image and Signal Denoising Methods
Papers in
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- Semantic Web and Ontologies 4
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- Image and Signal Denoising Methods 3
- Co-authors
- Gabriele Steidl (4 shared papers)Christoph Schnörr (3 shared papers)Gabriele Steidl (1 shared paper)Carl-Christian Kanne (2 shared papers)Till Westmann (2 shared papers)Guido Moerkotte (2 shared papers)Sven Helmer (2 shared papers)Stephan Didas (1 shared paper)
- Journals
- The Geneva Papers on Risk and Insurance Issues and Practice (1 paper)Pattern Recognition (1 paper)Space Policy (1 paper)International Journal of Computer Vision (1 paper)Sensors (1 paper)
- Partner nations
- GermanySwitzerlandFrance
In The Last Decade
Julia Neumann
13 papers receiving 385 citations
Peers
Comparison fields: 5 of 76
- Signal Processing 95
- Computer Vision and Pattern Recognition 148
- Artificial Intelligence 212
- Computer Networks and Communications 110
- Computational Mechanics 65
Countries citing papers authored by Julia Neumann
This map shows the geographic impact of Julia Neumann'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 Julia Neumann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia Neumann more than expected).
Fields of papers citing papers by Julia Neumann
This network shows the impact of papers produced by Julia Neumann. 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 Julia Neumann. The network helps show where Julia Neumann may publish in the future.
Co-authors
The 15 scholars most cited alongside Julia Neumann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 190 | |
| 2 | 2002 | 102 | |
| 3 | 2006 | 38 | |
| 4 | 2005 | 33 | |
| 5 | 2021 | 14 | |
| 6 | 2020 | 12 | |
| 7 | 2005 | 10 | |
| 8 | 2016 | 6 | |
| 9 | 2023 | 5 | |
| 10 | Natix: A Technology Overview | 2002 | 3 |
| 11 | Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification | 2003 | 2 |
| 12 | 2015 | 1 | |
| 13 | 2018 | 1 | |
| 14 | 2005 | 0 |
About Julia Neumann
Julia Neumann is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Geophysics and Computer Networks and Communications, having authored 14 papers that have together received 417 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (4 papers), Image and Signal Denoising Methods (3 papers), Geophysical and Geoelectrical Methods (2 papers), Scientific Computing and Data Management (2 papers), Seismic Imaging and Inversion Techniques (2 papers), Geophysical Methods and Applications (2 papers), Blind Source Separation Techniques (2 papers) and Advanced Database Systems and Queries (2 papers). The work is most often cited by research in Signal Processing (95 citations), Computer Vision and Pattern Recognition (148 citations), Artificial Intelligence (212 citations), Computer Networks and Communications (110 citations) and Computational Mechanics (65 citations). Julia Neumann has collaborated with scholars based in Germany, Switzerland and France. Frequent co-authors include Gabriele Steidl, Christoph Schnörr, Gabriele Steidl, Carl-Christian Kanne, Till Westmann, Guido Moerkotte, Sven Helmer, Stephan Didas, Sascha Eichstädt and Adrian Paschke. Their work appears in journals such as The Geneva Papers on Risk and Insurance Issues and Practice, Pattern Recognition, Space Policy, International Journal of Computer Vision and Sensors.
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.