Martin Schels
- Experimental and Cognitive Psychology top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Social Psychology
- Co-authors
- Friedhelm SchwenkerMarkus KächeleMichael GlodekGünther PalmPatrick ThiamStefan SchererSteffen WalterStephan Tschechne
- Topics
- Emotion and Mood Recognition (8 papers)Speech and Audio Processing (6 papers)Face and Expression Recognition (5 papers)
- Cited by
- Experimental and Cognitive PsychologySignal ProcessingComputer Vision and Pattern Recognition
- Journals
- Computational StatisticsJournal on Multimodal User InterfacesThe European Symposium on Artificial Neural Networks
- Partner nations
- GermanyUnited StatesIreland
In The Last Decade
Martin Schels
18 papers receiving 206 citations
Peers
Comparison fields: 5 of 49
- Experimental and Cognitive Psychology 120
- Artificial Intelligence 99
- Computer Vision and Pattern Recognition 63
- Signal Processing 58
- Social Psychology 38
Countries citing papers authored by Martin Schels
This map shows the geographic impact of Martin Schels'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 Martin Schels with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Schels more than expected).
Fields of papers citing papers by Martin Schels
This network shows the impact of papers produced by Martin Schels. 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 Martin Schels. The network helps show where Martin Schels may publish in the future.
Co-authorship network of co-authors of Martin Schels
This figure shows the co-authorship network connecting the top 25 collaborators of Martin Schels. A scholar is included among the top collaborators of Martin Schels 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 Martin Schels. Martin Schels is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 14 | |
| 3 | 2 | |
| 4 | 10 | |
| 5 | On the effects of continuous annotation tools and the human factor on the annotation outcome. | 1 |
| 6 | 24 | |
| 7 | 32 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | 9 | |
| 11 | 21 | |
| 12 | 12 | |
| 13 | 21 | |
| 14 | 27 | |
| 15 | Training of multiple classifier systems utilizing partially labeled sequential data sets. | 1 |
| 16 | 11 | |
| 17 | 12 | |
| 18 | 15 |
About Martin Schels
Martin Schels is a scholar working on Signal Processing, Experimental and Cognitive Psychology and Artificial Intelligence, having authored 18 papers that have together received 223 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (8 papers), Speech and Audio Processing (6 papers) and Face and Expression Recognition (5 papers). The work is most often cited by research in Experimental and Cognitive Psychology (120 citations), Signal Processing (58 citations) and Computer Vision and Pattern Recognition (63 citations). Martin Schels has collaborated with scholars based in Germany, United States and Ireland. Frequent co-authors include Friedhelm Schwenker, Markus Kächele, Michael Glodek, Günther Palm, Patrick Thiam, Stefan Scherer, Steffen Walter, Stephan Tschechne, Georg Layher and Tobias Brosch. Their work appears in journals such as Computational Statistics, Journal on Multimodal User Interfaces and The European Symposium on Artificial Neural Networks.
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.