Felipe Codevilla
- Computer Vision and Pattern Recognition top 5%
- Automotive Engineering top 10%
- Aerospace Engineering
- Artificial Intelligence
- Ocean Engineering top 10%
- Co-authors
- Antonio M. LópezSílvia Silva da Costa BotelhoOnay UrfalıoǧluAmanda DuartePaulo DrewsPedro L. BallesterFelix HeideChristopher Pal
- Topics
- Image Enhancement Techniques (3 papers)Modular Robots and Swarm Intelligence (2 papers)Advanced Image Fusion Techniques (2 papers)
- Journals
- IEEE Transactions on Intelligent Transportation SystemsIFAC-PapersOnLinearXiv (Cornell University)
In The Last Decade
Felipe Codevilla
8 papers receiving 360 citations
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 231
- Automotive Engineering 94
- Aerospace Engineering 86
- Artificial Intelligence 69
- Ocean Engineering 60
Countries citing papers authored by Felipe Codevilla
This map shows the geographic impact of Felipe Codevilla'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 Felipe Codevilla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Codevilla more than expected).
Fields of papers citing papers by Felipe Codevilla
This network shows the impact of papers produced by Felipe Codevilla. 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 Felipe Codevilla. The network helps show where Felipe Codevilla may publish in the future.
Co-authorship network of co-authors of Felipe Codevilla
This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Codevilla. A scholar is included among the top collaborators of Felipe Codevilla 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 Felipe Codevilla. Felipe Codevilla is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | Latent Variable Nested Set Transformers & AutoBots. | 0 |
| 3 | Action-based Representation Learning for Autonomous Driving. | 1 |
| 4 | 186 | |
| 5 | 86 | |
| 6 | 29 | |
| 7 | 45 | |
| 8 | 15 | |
| 9 | 2 |
About Felipe Codevilla
Felipe Codevilla is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Automotive Engineering, having authored 9 papers that have together received 371 indexed citations. Recurring topics across this work include Image Enhancement Techniques (3 papers), Modular Robots and Swarm Intelligence (2 papers) and Advanced Image Fusion Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (231 citations), Automotive Engineering (94 citations) and Media Technology (53 citations). Felipe Codevilla has collaborated with scholars based in Brazil, Germany and Canada. Frequent co-authors include Antonio M. López, Sílvia Silva da Costa Botelho, Onay Urfalıoǧlu, Amanda Duarte, Paulo Drews, Pedro L. Ballester, Felix Heide, Christopher Pal, Samira Ebrahimi Kahou and Florian Golemo. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, IFAC-PapersOnLine and arXiv (Cornell University).
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.