Hartmut Bauermeister
- Computational Mechanics
- Mathematical Physics
- Computer Vision and Pattern Recognition
- Geophysics
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Michael MoellerMatthias KahlMichael MöllerMartin BurgerMartin BenningAndreas KolbDaniel CremersP. Haring Bolívar
- Topics
- Sparse and Compressive Sensing Techniques (2 papers)Numerical methods in inverse problems (2 papers)Advanced Image and Video Retrieval Techniques (1 paper)
- Journals
- SIAM Journal on Imaging SciencesCommunications on Applied Mathematics and Computation2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Partner nations
- GermanyUnited KingdomJapan
In The Last Decade
Hartmut Bauermeister
4 papers receiving 11 citations
Peers
Comparison fields: 5 of 13
- Computational Mechanics 7
- Mathematical Physics 5
- Computer Vision and Pattern Recognition 5
- Geophysics 2
- Radiology, Nuclear Medicine and Imaging 2
Countries citing papers authored by Hartmut Bauermeister
This map shows the geographic impact of Hartmut Bauermeister'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 Hartmut Bauermeister with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hartmut Bauermeister more than expected).
Fields of papers citing papers by Hartmut Bauermeister
This network shows the impact of papers produced by Hartmut Bauermeister. 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 Hartmut Bauermeister. The network helps show where Hartmut Bauermeister may publish in the future.
Co-authorship network of co-authors of Hartmut Bauermeister
This figure shows the co-authorship network connecting the top 25 collaborators of Hartmut Bauermeister. A scholar is included among the top collaborators of Hartmut Bauermeister based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hartmut Bauermeister. Hartmut Bauermeister is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 3 | |
| 3 | 3 | |
| 4 | Learning Spectral Regularizations for Linear Inverse Problems | 1 |
About Hartmut Bauermeister
Hartmut Bauermeister is a scholar working on Mathematical Physics, Computational Mechanics and Astronomy and Astrophysics, having authored 4 papers that have together received 11 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (2 papers), Numerical methods in inverse problems (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Mathematical Physics (5 citations), Computational Mechanics (7 citations) and Computer Vision and Pattern Recognition (5 citations). Hartmut Bauermeister has collaborated with scholars based in Germany, United Kingdom and Japan. Frequent co-authors include Michael Moeller, Matthias Kahl, Michael Möller, Martin Burger, Martin Benning, Andreas Kolb, Daniel Cremers and P. Haring Bolívar. Their work appears in journals such as SIAM Journal on Imaging Sciences, Communications on Applied Mathematics and Computation and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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.