Hae‐Gon Jeon
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- Advanced Vision and Imaging 27
- Optical measurement and interference techniques 16
- Advanced Image Processing Techniques 10
- Image Enhancement Techniques 5
- Video Surveillance and Tracking Methods 4
- Media Technology top 0.5%
- Image Processing Techniques and Applications 16
- Instrumentation top 10%
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety 4
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- Robotics and Sensor-Based Localization 7
- Co-authors
- In So KweonYoung‐Jin YoonYunsu BokJoon‐Young LeeDonggeun YooInhwan BaeJaesik ParkGyeongmin Choe
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyComputer Graphics and Computer-Aided Design
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (7 papers)IEEE Transactions on Image Processing (3 papers)IEEE Access (1 paper)
- Partner nations
- South KoreaUnited StatesCanada
In The Last Decade
Hae‐Gon Jeon
49 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 78
- Computer Vision and Pattern Recognition 1.4k
- Media Technology 514
- Computer Graphics and Computer-Aided Design 59
- Instrumentation 58
- Automotive Engineering 140
Countries citing papers authored by Hae‐Gon Jeon
This map shows the geographic impact of Hae‐Gon Jeon'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 Hae‐Gon Jeon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hae‐Gon Jeon more than expected).
Fields of papers citing papers by Hae‐Gon Jeon
This network shows the impact of papers produced by Hae‐Gon Jeon. 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 Hae‐Gon Jeon. The network helps show where Hae‐Gon Jeon may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hae‐Gon Jeon, 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 | 2025 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 10 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 14 | |
| 8 | 2023 | 16 | |
| 9 | 2023 | 3 | |
| 10 | 2023 | 35 | |
| 11 | 2023 | 25 | |
| 12 | 2022 | 32 | |
| 13 | 2022 | 40 | |
| 14 | 2021 | 38 | |
| 15 | 2019 | 25 | |
| 16 | 2017 | 15 | |
| 17 | 2017 | 5 | |
| 18 | 2017 | 25 | |
| 19 | 2016 | 13 | |
| 20 | 2014 | 4 |
About Hae‐Gon Jeon
Hae‐Gon Jeon is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Biophysics, having authored 51 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (27 papers), Optical measurement and interference techniques (16 papers), Image Processing Techniques and Applications (16 papers), Advanced Image Processing Techniques (10 papers), Robotics and Sensor-Based Localization (7 papers), Image Enhancement Techniques (5 papers), Autonomous Vehicle Technology and Safety (4 papers) and Video Surveillance and Tracking Methods (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Media Technology (514 citations) and Computer Graphics and Computer-Aided Design (59 citations). Hae‐Gon Jeon has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include In So Kweon, Young‐Jin Yoon, Yunsu Bok, Joon‐Young Lee, Donggeun Yoo, Inhwan Bae, Jaesik Park, Gyeongmin Choe, Sunghoon Im and Jinsun Park. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.
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.