Carlos Vallespi-Gonzalez
- Computer Vision and Pattern Recognition top 10%
- Automotive Engineering
- Computational Mechanics
- Environmental Engineering
- Aerospace Engineering
- Co-authors
- Carl WellingtonSiheng ChenBaoan LiuChen FengNemanja DjuricDarshan HegdeYi ShiFang‐Chieh Chou
- Topics
- Autonomous Vehicle Technology and Safety (4 papers)Advanced Neural Network Applications (4 papers)Video Surveillance and Tracking Methods (3 papers)
- Journals
- IEEE Signal Processing Magazine2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Partner nations
- United States
In The Last Decade
Carlos Vallespi-Gonzalez
7 papers receiving 192 citations
Peers
Comparison fields: 5 of 46
- Computer Vision and Pattern Recognition 98
- Automotive Engineering 44
- Computational Mechanics 43
- Environmental Engineering 42
- Aerospace Engineering 41
Countries citing papers authored by Carlos Vallespi-Gonzalez
This map shows the geographic impact of Carlos Vallespi-Gonzalez'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 Carlos Vallespi-Gonzalez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carlos Vallespi-Gonzalez more than expected).
Fields of papers citing papers by Carlos Vallespi-Gonzalez
This network shows the impact of papers produced by Carlos Vallespi-Gonzalez. 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 Carlos Vallespi-Gonzalez. The network helps show where Carlos Vallespi-Gonzalez may publish in the future.
Co-authorship network of co-authors of Carlos Vallespi-Gonzalez
This figure shows the co-authorship network connecting the top 25 collaborators of Carlos Vallespi-Gonzalez. A scholar is included among the top collaborators of Carlos Vallespi-Gonzalez 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 Carlos Vallespi-Gonzalez. Carlos Vallespi-Gonzalez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 42 | |
| 2 | 2 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 12 | |
| 6 | 134 | |
| 7 | 1 |
About Carlos Vallespi-Gonzalez
Carlos Vallespi-Gonzalez is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Instrumentation, having authored 7 papers that have together received 197 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (4 papers), Advanced Neural Network Applications (4 papers) and Video Surveillance and Tracking Methods (3 papers). The work is most often cited by research in Geology (41 citations), Computer Vision and Pattern Recognition (98 citations) and Computer Graphics and Computer-Aided Design (14 citations). Carlos Vallespi-Gonzalez has collaborated with scholars based in United States. Frequent co-authors include Carl Wellington, Siheng Chen, Baoan Liu, Chen Feng, Nemanja Djuric, Darshan Hegde, Yi Shi, Fang‐Chieh Chou, Chao Wang and Henggang Cui. Their work appears in journals such as IEEE Signal Processing Magazine, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2022 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.