Jun‐Hai Yong
Impact in
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- Computer Graphics and Visualization Techniques
- Computational Geometry and Mesh Generation
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- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
Papers in
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- Computer Graphics and Visualization Techniques 37
- Computational Geometry and Mesh Generation 32
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- Advanced Numerical Analysis Techniques 58
- 3D Shape Modeling and Analysis 34
Jun‐Hai Yong
122 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 129
- Computer Graphics and Computer-Aided Design 549
- Computer Vision and Pattern Recognition 794
- Computational Mechanics 753
- Geology 105
- Industrial and Manufacturing Engineering 145
Countries citing papers authored by Jun‐Hai Yong
This map shows the geographic impact of Jun‐Hai Yong'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 Jun‐Hai Yong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun‐Hai Yong more than expected).
Fields of papers citing papers by Jun‐Hai Yong
This network shows the impact of papers produced by Jun‐Hai Yong. 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 Jun‐Hai Yong. The network helps show where Jun‐Hai Yong may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun‐Hai Yong, 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 | 2024 | 0 | |
| 2 | Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation Hit paper breakdown → | 2024 | 57 |
| 3 | 2024 | 6 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 1 | |
| 7 | 2023 | 2 | |
| 8 | 2023 | 11 | |
| 9 | 2023 | 1 | |
| 10 | 2021 | 26 | |
| 11 | Shadow Detection Using Robust Texture Learning. | 2018 | 1 |
| 12 | 2017 | 9 | |
| 13 | Constructing G^1 quadratic Bezier curves with arbitrary endpoint tangent vectors | 2010 | 3 |
| 14 | 2008 | 17 | |
| 15 | 2008 | 89 | |
| 16 | 2008 | 8 | |
| 17 | 2008 | 16 | |
| 18 | 2007 | 2 | |
| 19 | 2006 | 50 | |
| 20 | Efficient algorithm for general polygon clipping | 2005 | 1 |
About Jun‐Hai Yong
Jun‐Hai Yong is a scholar working on Computer Graphics and Computer-Aided Design, Computational Mechanics, Computer Vision and Pattern Recognition, Human-Computer Interaction and Industrial and Manufacturing Engineering, having authored 127 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Numerical Analysis Techniques (58 papers), Computer Graphics and Visualization Techniques (37 papers), 3D Shape Modeling and Analysis (34 papers), Computational Geometry and Mesh Generation (32 papers), Advanced Vision and Imaging (15 papers), Human Pose and Action Recognition (11 papers), Manufacturing Process and Optimization (9 papers) and Advanced Neural Network Applications (9 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (549 citations), Computer Vision and Pattern Recognition (794 citations), Computational Mechanics (753 citations), Geology (105 citations) and Industrial and Manufacturing Engineering (145 citations). Jun‐Hai Yong has collaborated with scholars based in China, France and United States. Frequent co-authors include Jean‐Claude Paul, Jiaguang Sun, Zhizhong Wang, Fuhua Cheng, Feng Xu, Chen Ma, Yu-Shen Liu, Wen Zheng, Bin Wang and Li Chen. Their work appears in journals such as Computer-Aided Design, Computer Graphics Forum, Computers & Graphics, IEEE Transactions on Visualization and Computer Graphics and ACM Transactions on 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.