Jan C. van Gemert
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Cognitive Neuroscience
- Social Psychology
- Experimental and Cognitive Psychology
- Co-authors
- Victoria YanulevskayaNicu SebeKatharina RothJan‐Mark GeusebroekCees G. M. SnoekPascal MettesThomas MensinkSezer Karaoğlu
- Topics
- Image Retrieval and Classification Techniques (3 papers)Video Analysis and Summarization (2 papers)Anomaly Detection Techniques and Applications (2 papers)
- Journals
- IEEE Transactions on Image ProcessingComputer Vision and Image UnderstandingComputational Geosciences
- Partner nations
- NetherlandsGermanySpain
In The Last Decade
Jan C. van Gemert
9 papers receiving 234 citations
Peers
Comparison fields: 5 of 43
- Computer Vision and Pattern Recognition 181
- Artificial Intelligence 81
- Cognitive Neuroscience 27
- Social Psychology 23
- Experimental and Cognitive Psychology 20
Countries citing papers authored by Jan C. van Gemert
This map shows the geographic impact of Jan C. van Gemert'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 Jan C. van Gemert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan C. van Gemert more than expected).
Fields of papers citing papers by Jan C. van Gemert
This network shows the impact of papers produced by Jan C. van Gemert. 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 Jan C. van Gemert. The network helps show where Jan C. van Gemert may publish in the future.
Co-authorship network of co-authors of Jan C. van Gemert
This figure shows the co-authorship network connecting the top 25 collaborators of Jan C. van Gemert. A scholar is included among the top collaborators of Jan C. van Gemert 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 Jan C. van Gemert. Jan C. van Gemert 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 | 14 | |
| 3 | 22 | |
| 4 | 20 | |
| 5 | 2 | |
| 6 | 40 | |
| 7 | 141 | |
| 8 | 1 | |
| 9 | Interactive Search Using Indexing, Filtering, Browsing and Ranking | 1 |
| 10 | Practical tutorial for using Corba | 0 |
About Jan C. van Gemert
Jan C. van Gemert is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistics, Probability and Uncertainty, having authored 10 papers that have together received 245 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (3 papers), Video Analysis and Summarization (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (181 citations), Urban Studies (16 citations) and Artificial Intelligence (81 citations). Jan C. van Gemert has collaborated with scholars based in Netherlands, Germany and Spain. Frequent co-authors include Victoria Yanulevskaya, Nicu Sebe, Katharina Roth, Jan‐Mark Geusebroek, Cees G. M. Snoek, Pascal Mettes, Thomas Mensink, Sezer Karaoğlu, Ran Tao and Theo Gevers. Their work appears in journals such as IEEE Transactions on Image Processing, Computer Vision and Image Understanding and Computational Geosciences.
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.