Juil Sock
Impact in
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- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Image and Object Detection Techniques
- Advanced Vision and Imaging
- Robotic Path Planning Algorithms
- Aerospace Engineering top 5%
- Robotics and Sensor-Based Localization
Papers in
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- Advanced Vision and Imaging 4
- Advanced Image and Video Retrieval Techniques 3
- Image and Object Detection Techniques 2
- Human Pose and Action Recognition 2
- Advanced Data Compression Techniques 1
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- Robotics and Sensor-Based Localization 9
- Co-authors
- Tae‐Kyun Kim (7 shared papers)Guillermo Garcia-Hernando (4 shared papers)Caner Şahin (3 shared papers)Kiho Kwak (3 shared papers)Luís Seabra Lopes (2 shared papers)Jihong Min (2 shared papers)Jun Kim (1 shared paper)Hamidreza Kasaei (1 shared paper)
- Journals
- Sensors (1 paper)Expert Systems with Applications (1 paper)IEEE Transactions on Circuits and Systems for Video Technology (1 paper)Image and Vision Computing (1 paper)IEEE Transactions on Visualization and Computer Graphics (1 paper)
- Partner nations
- United KingdomSouth KoreaPortugal
In The Last Decade
Juil Sock
12 papers receiving 375 citations
Peers
Comparison fields: 5 of 39
- Computer Vision and Pattern Recognition 281
- Aerospace Engineering 222
- Control and Systems Engineering 165
- Geology 39
- Human-Computer Interaction 29
Countries citing papers authored by Juil Sock
This map shows the geographic impact of Juil Sock'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 Juil Sock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juil Sock more than expected).
Fields of papers citing papers by Juil Sock
This network shows the impact of papers produced by Juil Sock. 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 Juil Sock. The network helps show where Juil Sock may publish in the future.
Co-authors
The 20 scholars most cited alongside Juil Sock, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 76 | |
| 2 | 2016 | 66 | |
| 3 | 2017 | 64 | |
| 4 | 2017 | 50 | |
| 5 | 2016 | 38 | |
| 6 | 2020 | 25 | |
| 7 | 2018 | 23 | |
| 8 | 2019 | 19 | |
| 9 | 2020 | 15 | |
| 10 | 2024 | 6 | |
| 11 | 2024 | 3 | |
| 12 | 2014 | 2 | |
| 13 | 2024 | 0 |
About Juil Sock
Juil Sock is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Control and Systems Engineering, Automotive Engineering and Geology, having authored 13 papers that have together received 387 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (9 papers), Advanced Vision and Imaging (4 papers), Robot Manipulation and Learning (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Autonomous Vehicle Technology and Safety (2 papers), Image and Object Detection Techniques (2 papers), Human Pose and Action Recognition (2 papers) and Advanced Data Compression Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (281 citations), Aerospace Engineering (222 citations), Control and Systems Engineering (165 citations), Geology (39 citations) and Human-Computer Interaction (29 citations). Juil Sock has collaborated with scholars based in United Kingdom, South Korea and Portugal. Frequent co-authors include Tae‐Kyun Kim, Guillermo Garcia-Hernando, Caner Şahin, Kiho Kwak, Luís Seabra Lopes, Jihong Min, Jun Kim, Hamidreza Kasaei, Sungdae Sim and Rigas Kouskouridas. Their work appears in journals such as Sensors, Expert Systems with Applications, IEEE Transactions on Circuits and Systems for Video Technology, Image and Vision Computing and IEEE Transactions on Visualization and Computer Graphics.
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.