Shengkai Wu
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Media Technology top 10%
- Vehicle License Plate Recognition
Papers in
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- Advanced Image and Video Retrieval Techniques 4
- Advanced Neural Network Applications 4
- Video Surveillance and Tracking Methods 3
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- Vehicle License Plate Recognition 3
- Co-authors
- Xinggang Wang (2 shared papers)Xiaoping Li (1 shared paper)Xiaoping Li (4 shared papers)Chenxi Lin (2 shared papers)Jinrong Yang (3 shared papers)Chao Deng (1 shared paper)Xing Shao (1 shared paper)Liangliang Ren (2 shared papers)
In The Last Decade
Shengkai Wu
10 papers receiving 224 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 170
- Media Technology 31
- Industrial and Manufacturing Engineering 33
- Geology 9
- Artificial Intelligence 48
Countries citing papers authored by Shengkai Wu
This map shows the geographic impact of Shengkai Wu'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 Shengkai Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shengkai Wu more than expected).
Fields of papers citing papers by Shengkai Wu
This network shows the impact of papers produced by Shengkai Wu. 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 Shengkai Wu. The network helps show where Shengkai Wu may publish in the future.
Co-authors
The 18 scholars most cited alongside Shengkai Wu, 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 | 131 | |
| 2 | 2022 | 66 | |
| 3 | 2022 | 9 | |
| 4 | 2022 | 8 | |
| 5 | 2023 | 7 | |
| 6 | 2024 | 3 | |
| 7 | 2017 | 3 | |
| 8 | 2014 | 2 | |
| 9 | 2022 | 1 | |
| 10 | Surveillance system using abandoned luggage detection | 2007 | 1 |
| 11 | 2024 | 0 |
About Shengkai Wu
Shengkai Wu is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Organic Chemistry, Artificial Intelligence and Electrical and Electronic Engineering, having authored 11 papers that have together received 231 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (4 papers), Advanced Neural Network Applications (4 papers), Vehicle License Plate Recognition (3 papers), Video Surveillance and Tracking Methods (3 papers), Wood and Agarwood Research (2 papers), Imbalanced Data Classification Techniques (1 paper), 3D Surveying and Cultural Heritage (1 paper) and VLSI and FPGA Design Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (170 citations), Media Technology (31 citations), Industrial and Manufacturing Engineering (33 citations), Geology (9 citations) and Artificial Intelligence (48 citations). Shengkai Wu has collaborated with scholars based in China, Taiwan and Canada. Frequent co-authors include Xinggang Wang, Xiaoping Li, Xiaoping Li, Xiaoping Li, Chenxi Lin, Jinrong Yang, Chao Deng, Xing Shao, Liangliang Ren and Pan Wang. Their work appears in journals such as Image and Vision Computing, Sensors, IEEE Robotics and Automation Letters, The International Journal of Advanced Manufacturing Technology and Pattern Recognition Letters.
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.