Paul Swoboda
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Computer Networks and Communications
- Aerospace Engineering
- Computational Mechanics
- Co-authors
- Bogdan SavchynskyyFlorian BernardChristoph SchnörrDagmar KainmüllerChristian TheobaltBjoern AndresDaniel SonntagJörg Hendrik Kappes
- Topics
- Bayesian Modeling and Causal Inference (8 papers)Graph Theory and Algorithms (6 papers)Machine Learning and Algorithms (6 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Science ApplicationsComputer Graphics and Computer-Aided Design
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceSIAM Journal on Imaging SciencesJournal of Mathematical Imaging and Vision
- Partner nations
- GermanyAustriaUnited States
In The Last Decade
Paul Swoboda
21 papers receiving 224 citations
Peers
Comparison fields: 5 of 62
- Computer Vision and Pattern Recognition 127
- Artificial Intelligence 83
- Computer Networks and Communications 40
- Aerospace Engineering 30
- Computational Mechanics 28
Countries citing papers authored by Paul Swoboda
This map shows the geographic impact of Paul Swoboda'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 Paul Swoboda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Swoboda more than expected).
Fields of papers citing papers by Paul Swoboda
This network shows the impact of papers produced by Paul Swoboda. 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 Paul Swoboda. The network helps show where Paul Swoboda may publish in the future.
Co-authorship network of co-authors of Paul Swoboda
This figure shows the co-authorship network connecting the top 25 collaborators of Paul Swoboda. A scholar is included among the top collaborators of Paul Swoboda 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 Paul Swoboda. Paul Swoboda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | 2 | |
| 4 | 27 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 11 | |
| 8 | 19 | |
| 9 | 17 | |
| 10 | 1 | |
| 11 | 10 | |
| 12 | 26 | |
| 13 | 0 | |
| 14 | 12 | |
| 15 | 1 | |
| 16 | 7 | |
| 17 | Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation | 7 |
| 18 | 9 | |
| 19 | 1 | |
| 20 | 67 |
About Paul Swoboda
Paul Swoboda is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 22 papers that have together received 248 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (8 papers), Graph Theory and Algorithms (6 papers) and Machine Learning and Algorithms (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (127 citations), Computer Science Applications (25 citations) and Computer Graphics and Computer-Aided Design (10 citations). Paul Swoboda has collaborated with scholars based in Germany, Austria and United States. Frequent co-authors include Bogdan Savchynskyy, Florian Bernard, Christoph Schnörr, Dagmar Kainmüller, Christian Theobalt, Bjoern Andres, Daniel Sonntag, Jörg Hendrik Kappes, Hassan Abu Alhaija and Johan Thunberg. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Imaging Sciences and Journal of Mathematical Imaging and Vision.
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.