John Alejandro Castro-Vargas
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Human-Computer Interaction top 10%
- Atmospheric Science
- Global and Planetary Change
- Co-authors
- José García‐RodríguezAlberto García-GarcíaSergio Orts‐EscolanoSergiu OpreaPablo Martínez-GonzálezAntonis ArgyrosJorge Azorín-LópezFrancisco Gomez‐Donoso
- Topics
- Hand Gesture Recognition Systems (3 papers)Robot Manipulation and Learning (2 papers)Human Pose and Action Recognition (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionHuman-Computer InteractionIndustrial and Manufacturing Engineering
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligencePattern Recognition LettersComputers in Industry
- Partner nations
- SpainCosta RicaGreece
In The Last Decade
John Alejandro Castro-Vargas
6 papers receiving 242 citations
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 111
- Artificial Intelligence 40
- Human-Computer Interaction 28
- Atmospheric Science 27
- Global and Planetary Change 22
Countries citing papers authored by John Alejandro Castro-Vargas
This map shows the geographic impact of John Alejandro Castro-Vargas'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 John Alejandro Castro-Vargas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Alejandro Castro-Vargas more than expected).
Fields of papers citing papers by John Alejandro Castro-Vargas
This network shows the impact of papers produced by John Alejandro Castro-Vargas. 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 John Alejandro Castro-Vargas. The network helps show where John Alejandro Castro-Vargas may publish in the future.
Co-authorship network of co-authors of John Alejandro Castro-Vargas
This figure shows the co-authorship network connecting the top 25 collaborators of John Alejandro Castro-Vargas. A scholar is included among the top collaborators of John Alejandro Castro-Vargas 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 John Alejandro Castro-Vargas. John Alejandro Castro-Vargas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 34 | |
| 3 | 167 | |
| 4 | 2 | |
| 5 | 26 | |
| 6 | 16 |
About John Alejandro Castro-Vargas
John Alejandro Castro-Vargas is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Rehabilitation, having authored 6 papers that have together received 248 indexed citations. Recurring topics across this work include Hand Gesture Recognition Systems (3 papers), Robot Manipulation and Learning (2 papers) and Human Pose and Action Recognition (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (111 citations), Human-Computer Interaction (28 citations) and Industrial and Manufacturing Engineering (21 citations). John Alejandro Castro-Vargas has collaborated with scholars based in Spain, Costa Rica and Greece. Frequent co-authors include José García‐Rodríguez, Alberto García-García, Sergio Orts‐Escolano, Sergiu Oprea, Pablo Martínez-González, Antonis Argyros, Jorge Azorín-López, Francisco Gomez‐Donoso, Miguel Cazorla and Eugenio Aguirre. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition Letters and Computers in Industry.
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.