Julia Neumann
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
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- Face and Expression Recognition
- Image and Signal Denoising Methods
Papers in
-
- Image and Signal Denoising Methods 5
- Face and Expression Recognition 2
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- Semantic Web and Ontologies 4
- Co-authors
- Gabriele Steidl (6 shared papers)Christoph Schnörr (5 shared papers)Gabriele Steidl (2 shared papers)Guido Moerkotte (3 shared papers)Sven Helmer (3 shared papers)Till Westmann (3 shared papers)Carl-Christian Kanne (3 shared papers)Stephan Didas (2 shared papers)
- Journals
- The Geneva Papers on Risk and Insurance Issues and Practice (1 paper)International Journal of Computer Vision (1 paper)Sensors (1 paper)The VLDB Journal (1 paper)Machine Learning (1 paper)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Julia Neumann
17 papers receiving 508 citations
Peers
Comparison fields: 5 of 85
- Signal Processing 129
- Computer Vision and Pattern Recognition 195
- Artificial Intelligence 264
- Computer Networks and Communications 158
- Computational Mechanics 85
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 16 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 | 210 | |
| 2 | 2002 | 138 | |
| 3 | 2006 | 40 | |
| 4 | 2005 | 37 | |
| 5 | 2005 | 21 | |
| 6 | 2004 | 21 | |
| 7 | 2021 | 15 | |
| 8 | 2005 | 13 | |
| 9 | 2020 | 12 | |
| 10 | 2003 | 11 | |
| 11 | 2023 | 6 | |
| 12 | 2016 | 6 | |
| 13 | 2018 | 6 | |
| 14 | Natix: A Technology Overview | 2002 | 3 |
| 15 | Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification | 2003 | 3 |
| 16 | 2003 | 2 | |
| 17 | 2015 | 1 | |
| 18 | 2019 | 0 | |
| 19 | 2005 | 0 |
About Julia Neumann
Julia Neumann is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Computational Mechanics and Computer Networks and Communications, having authored 19 papers that have together received 545 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (5 papers), Semantic Web and Ontologies (4 papers), Advanced Database Systems and Queries (3 papers), Blind Source Separation Techniques (3 papers), Sparse and Compressive Sensing Techniques (3 papers), Geophysical and Geoelectrical Methods (2 papers), Seismic Imaging and Inversion Techniques (2 papers) and Face and Expression Recognition (2 papers). The work is most often cited by research in Signal Processing (129 citations), Computer Vision and Pattern Recognition (195 citations), Artificial Intelligence (264 citations), Computer Networks and Communications (158 citations) and Computational Mechanics (85 citations). Julia Neumann has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Gabriele Steidl, Christoph Schnörr, Gabriele Steidl, Guido Moerkotte, Sven Helmer, Till Westmann, Carl-Christian Kanne, Stephan Didas, Sascha Eichstädt and Adrian Paschke. Their work appears in journals such as The Geneva Papers on Risk and Insurance Issues and Practice, International Journal of Computer Vision, Sensors, The VLDB Journal and Machine Learning.
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.