Kailing Guo
Impact in
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- Image Enhancement Techniques
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Face and Expression Recognition
- Image and Signal Denoising Methods
Papers in
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- Advanced Neural Network Applications 6
- Advanced Vision and Imaging 6
- Image Enhancement Techniques 6
- Face and Expression Recognition 5
- Video Surveillance and Tracking Methods 4
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- Sparse and Compressive Sensing Techniques 5
- Co-authors
- Dacheng Tao (3 shared papers)Bolun Cai (7 shared papers)Xiangmin Xu (28 shared papers)Kui Jia (2 shared papers)Bin Hu (1 shared paper)Xiaofen Xing (18 shared papers)Liu Liu (1 shared paper)Dong Xu (1 shared paper)
In The Last Decade
Kailing Guo
32 papers receiving 389 citations
Peers
Comparison fields: 5 of 53
- Computer Vision and Pattern Recognition 296
- Computational Mathematics 8
- Media Technology 96
- Experimental and Cognitive Psychology 43
- Computer Graphics and Computer-Aided Design 9
Countries citing papers authored by Kailing Guo
This map shows the geographic impact of Kailing Guo'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 Kailing Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kailing Guo more than expected).
Fields of papers citing papers by Kailing Guo
This network shows the impact of papers produced by Kailing Guo. 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 Kailing Guo. The network helps show where Kailing Guo may publish in the future.
Co-authors
The 25 scholars most cited alongside Kailing Guo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 207 | |
| 2 | 2017 | 40 | |
| 3 | 2024 | 19 | |
| 4 | 2023 | 18 | |
| 5 | 2015 | 13 | |
| 6 | 2017 | 10 | |
| 7 | 2019 | 9 | |
| 8 | 2022 | 8 | |
| 9 | 2019 | 8 | |
| 10 | 2024 | 7 | |
| 11 | 2023 | 7 | |
| 12 | 2022 | 5 | |
| 13 | 2021 | 5 | |
| 14 | 2022 | 5 | |
| 15 | 2019 | 4 | |
| 16 | 2018 | 4 | |
| 17 | 2022 | 3 | |
| 18 | 2021 | 3 | |
| 19 | 2025 | 2 | |
| 20 | 2024 | 2 |
About Kailing Guo
Kailing Guo is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Artificial Intelligence, Experimental and Cognitive Psychology and Cognitive Neuroscience, having authored 36 papers that have together received 398 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (8 papers), Advanced Neural Network Applications (6 papers), EEG and Brain-Computer Interfaces (6 papers), Advanced Vision and Imaging (6 papers), Image Enhancement Techniques (6 papers), Face and Expression Recognition (5 papers), Sparse and Compressive Sensing Techniques (5 papers) and Video Surveillance and Tracking Methods (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (296 citations), Computational Mathematics (8 citations), Media Technology (96 citations), Experimental and Cognitive Psychology (43 citations) and Computer Graphics and Computer-Aided Design (9 citations). Kailing Guo has collaborated with scholars based in China, Australia and Italy. Frequent co-authors include Dacheng Tao, Bolun Cai, Xiangmin Xu, Kui Jia, Bin Hu, Xiaofen Xing, Liu Liu, Dong Xu, Ziyang Zhang and Yuan Zhang. Their work appears in journals such as Frontiers in Neuroscience, IEEE Access, IEEE Transactions on Multimedia, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Circuits and Systems for Video Technology.
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.