Jürgen Schürmann
- Molecular Biology
- Neurology top 5%
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Cancer Research
- Co-authors
- Geoffrey J. McLachlanW. LutzM. SchwabAchim WenzelE. MandlerThomas A. BayerJ. FrankeJürgen Franke
- Topics
- Handwritten Text Recognition Techniques (2 papers)AI in cancer detection (1 paper)Image Retrieval and Classification Techniques (1 paper)
- Partner nations
- Germany
In The Last Decade
Jürgen Schürmann
7 papers receiving 446 citations
Peers
Comparison fields: 5 of 83
- Molecular Biology 207
- Neurology 199
- Computer Vision and Pattern Recognition 115
- Artificial Intelligence 88
- Cancer Research 84
Countries citing papers authored by Jürgen Schürmann
This map shows the geographic impact of Jürgen Schürmann'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 Jürgen Schürmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jürgen Schürmann more than expected).
Fields of papers citing papers by Jürgen Schürmann
This network shows the impact of papers produced by Jürgen Schürmann. 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 Jürgen Schürmann. The network helps show where Jürgen Schürmann may publish in the future.
Co-authorship network of co-authors of Jürgen Schürmann
This figure shows the co-authorship network connecting the top 25 collaborators of Jürgen Schürmann. A scholar is included among the top collaborators of Jürgen Schürmann 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 Jürgen Schürmann. Jürgen Schürmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Segmentation-free detection of overtaking vehicles with a two-stage time-delay neural network classifier. | 3 |
| 2 | Dimensionality reduction by local processing. | 1 |
| 3 | 124 | |
| 4 | Conditional expression of N-myc in human neuroblastoma cells increases expression of alpha-prothymosin and ornithine decarboxylase and accelerates progression into S-phase early after mitogenic stimulation of quiescent cells. | 260 |
| 5 | Document analysis—from pixels to contents | 23 |
| 6 | 52 | |
| 7 | 1 |
About Jürgen Schürmann
Jürgen Schürmann is a scholar working on Biophysics, Computer Vision and Pattern Recognition and Media Technology, having authored 7 papers that have together received 464 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (2 papers), AI in cancer detection (1 paper) and Image Retrieval and Classification Techniques (1 paper). The work is most often cited by research in Neurology (199 citations), Computer Vision and Pattern Recognition (115 citations) and Cancer Research (84 citations). Jürgen Schürmann has collaborated with scholars based in Germany. Frequent co-authors include Geoffrey J. McLachlan, W. Lutz, M. Schwab, Achim Wenzel, E. Mandler, Thomas A. Bayer, J. Franke, Jürgen Franke, Joachim K. Anlauf and Christian Wöhler. Their work appears in journals such as Proceedings of the IEEE, Biometrics and Pattern Recognition.
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.