András Rövid
Impact in
- Computational Mathematics top 5%
- Tensor decomposition and applications
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- Image Enhancement Techniques
- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
Papers in
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- Advanced Vision and Imaging 14
- Image Enhancement Techniques 10
- Optical measurement and interference techniques 8
- Advanced Neural Network Applications 7
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- Vehicle Dynamics and Control Systems 6
- Co-authors
- Annamária R. Várkonyi-Kóczy (17 shared papers)Péter Várlaki (21 shared papers)Takeshi Hashimoto (18 shared papers)László Szeidl (11 shared papers)Zsolt Szalay (7 shared papers)M.G. Ruano (3 shared papers)Viktor Tihanyi (3 shared papers)István Harmati (5 shared papers)
In The Last Decade
András Rövid
65 papers receiving 325 citations
Peers
Comparison fields: 5 of 62
- Computational Mathematics 32
- Computer Vision and Pattern Recognition 189
- Media Technology 52
- Automotive Engineering 50
- Geology 17
Countries citing papers authored by András Rövid
This map shows the geographic impact of András Rövid'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 András Rövid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites András Rövid more than expected).
Fields of papers citing papers by András Rövid
This network shows the impact of papers produced by András Rövid. 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 András Rövid. The network helps show where András Rövid may publish in the future.
Co-authors
The 25 scholars most cited alongside András Rövid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 43 | |
| 2 | 2021 | 33 | |
| 3 | 2007 | 16 | |
| 4 | 2013 | 15 | |
| 5 | 2005 | 14 | |
| 6 | 2019 | 12 | |
| 7 | 2011 | 12 | |
| 8 | 2010 | 11 | |
| 9 | 2007 | 11 | |
| 10 | 2011 | 10 | |
| 11 | 2005 | 9 | |
| 12 | Real-time and high precision 3D shape measurement method | 2013 | 8 |
| 13 | 2006 | 8 | |
| 14 | 2005 | 8 | |
| 15 | 2013 | 7 | |
| 16 | 2020 | 7 | |
| 17 | 2006 | 7 | |
| 18 | 2020 | 7 | |
| 19 | 2006 | 6 | |
| 20 | 2020 | 5 |
About András Rövid
András Rövid is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Aerospace Engineering, Artificial Intelligence and Mechanical Engineering, having authored 72 papers that have together received 349 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (14 papers), Image Enhancement Techniques (10 papers), Robotics and Sensor-Based Localization (9 papers), Tensor decomposition and applications (8 papers), Optical measurement and interference techniques (8 papers), Advanced Neural Network Applications (7 papers), Neural Networks and Applications (7 papers) and Vehicle Dynamics and Control Systems (6 papers). The work is most often cited by research in Computational Mathematics (32 citations), Computer Vision and Pattern Recognition (189 citations), Media Technology (52 citations), Automotive Engineering (50 citations) and Geology (17 citations). András Rövid has collaborated with scholars based in Hungary, Japan and Portugal. Frequent co-authors include Annamária R. Várkonyi-Kóczy, Péter Várlaki, Takeshi Hashimoto, László Szeidl, Zsolt Szalay, M.G. Ruano, Viktor Tihanyi, István Harmati, Imre J. Rudas and Balázs Varga. Their work appears in journals such as Acta Polytechnica Hungarica, IEEE Access, IEEE Transactions on Instrumentation and Measurement, Asian Journal of Control and Energies.
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.