Yajun Chen
- Computer Vision and Pattern Recognition top 2%
- Industrial and Manufacturing Engineering top 2%
- Plant Science top 10%
- Media Technology top 2%
- Artificial Intelligence top 10%
- Topics
- Industrial Vision Systems and Defect Detection (11 papers)Digital Media Forensic Detection (9 papers)Advanced Steganography and Watermarking Techniques (7 papers)
- Cited by
- Computer Vision and Pattern RecognitionIndustrial and Manufacturing EngineeringMedia Technology
- Partner nations
- ChinaUnited States
In The Last Decade
Yajun Chen
49 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 99
- Computer Vision and Pattern Recognition 614
- Industrial and Manufacturing Engineering 276
- Plant Science 223
- Media Technology 139
- Artificial Intelligence 132
Countries citing papers authored by Yajun Chen
This map shows the geographic impact of Yajun Chen'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 Yajun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yajun Chen more than expected).
Fields of papers citing papers by Yajun Chen
This network shows the impact of papers produced by Yajun Chen. 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 Yajun Chen. The network helps show where Yajun Chen may publish in the future.
Co-authorship network of co-authors of Yajun Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Yajun Chen. A scholar is included among the top collaborators of Yajun Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yajun Chen. Yajun Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 7 | |
| 4 | 1 | |
| 5 | Surface Defect Detection Methods for Industrial Products: A Reviewbreakdown → | 225 |
| 6 | Review of Weed Detection Methods Based on Computer Visionbreakdown → | 185 |
| 7 | 63 | |
| 8 | 15 | |
| 9 | 10 | |
| 10 | 18 | |
| 11 | 17 | |
| 12 | 47 | |
| 13 | 30 | |
| 14 | 92 | |
| 15 | 5 | |
| 16 | Feature Extraction Methods on Facial Expression Recognition | 1 |
| 17 | System Model and Integration of JDF Workflow | 1 |
| 18 | Defect Detecting of Printing Matter Based on Interest Points Feature Matching | 3 |
| 19 | A Study of EM Learning Algorithm Based on Gaussian Mixture Model | 1 |
| 20 | A Study of SVM-Constructions Based on Kernels | 1 |
About Yajun Chen
Yajun Chen is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Media Technology, having authored 51 papers that have together received 1.1k indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (11 papers), Digital Media Forensic Detection (9 papers) and Advanced Steganography and Watermarking Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (614 citations), Industrial and Manufacturing Engineering (276 citations) and Media Technology (139 citations). Yajun Chen has collaborated with scholars based in China and United States. Frequent co-authors include Xiaobing Kang, Fan Zhao, Yuanyuan Ding, Guangfeng Lin, Bo Zhao, Erhu Zhang, Linhao Shao, Erhu Zhang, Yilan Wang and Kaiyang Liao. Their work appears in journals such as IEEE Access, Sensors and Pattern Recognition.
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.