Vladimir Somers
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Artificial Intelligence
- Economics and Econometrics
- Signal Processing
- Co-authors
- Alexandre AlahiChristophe De VleeschouwerXin ZhouAnthony CioppaSilvio GiancolaBernard GhanemShohreh KasaeiMarc Van Droogenbroeck
- Topics
- Human Pose and Action Recognition (2 papers)Advanced Neural Network Applications (2 papers)Anomaly Detection Techniques and Applications (2 papers)
- Journals
- Infoscience (Ecole Polytechnique Fédérale de Lausanne)Open Repository and Bibliography (University of Liège)2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Partner nations
- CanadaChinaSaudi Arabia
In The Last Decade
Vladimir Somers
3 papers receiving 98 citations
Peers
Comparison fields: 5 of 24
- Computer Vision and Pattern Recognition 89
- Biomedical Engineering 39
- Artificial Intelligence 17
- Economics and Econometrics 11
- Signal Processing 5
Countries citing papers authored by Vladimir Somers
This map shows the geographic impact of Vladimir Somers'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 Vladimir Somers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimir Somers more than expected).
Fields of papers citing papers by Vladimir Somers
This network shows the impact of papers produced by Vladimir Somers. 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 Vladimir Somers. The network helps show where Vladimir Somers may publish in the future.
Co-authorship network of co-authors of Vladimir Somers
This figure shows the co-authorship network connecting the top 25 collaborators of Vladimir Somers. A scholar is included among the top collaborators of Vladimir Somers 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 Vladimir Somers. Vladimir Somers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 7 | |
| 3 | 82 |
About Vladimir Somers
Vladimir Somers is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Economics and Econometrics, having authored 3 papers that have together received 100 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (2 papers), Advanced Neural Network Applications (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (89 citations), Biomedical Engineering (39 citations) and Artificial Intelligence (17 citations). Vladimir Somers has collaborated with scholars based in Canada, China and Saudi Arabia. Frequent co-authors include Alexandre Alahi, Christophe De Vleeschouwer, Xin Zhou, Anthony Cioppa, Silvio Giancola, Bernard Ghanem, Shohreh Kasaei and Marc Van Droogenbroeck. Their work appears in journals such as Infoscience (Ecole Polytechnique Fédérale de Lausanne), Open Repository and Bibliography (University of Liège) and 2023 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.