Xiaopeng Yan
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 10%
- Computational Theory and Mathematics top 5%
- Mathematical Physics top 10%
- Topics
- Chaos-based Image/Signal Encryption (13 papers)Advanced Steganography and Watermarking Techniques (8 papers)Cellular Automata and Applications (5 papers)
In The Last Decade
Xiaopeng Yan
19 papers receiving 801 citations
Hit Papers
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 651
- Artificial Intelligence 271
- Statistical and Nonlinear Physics 89
- Computational Theory and Mathematics 83
- Mathematical Physics 66
Countries citing papers authored by Xiaopeng Yan
This map shows the geographic impact of Xiaopeng Yan'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 Xiaopeng Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaopeng Yan more than expected).
Fields of papers citing papers by Xiaopeng Yan
This network shows the impact of papers produced by Xiaopeng Yan. 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 Xiaopeng Yan. The network helps show where Xiaopeng Yan may publish in the future.
Co-authorship network of co-authors of Xiaopeng Yan
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaopeng Yan. A scholar is included among the top collaborators of Xiaopeng Yan 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 Xiaopeng Yan. Xiaopeng Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 38 | |
| 4 | 8 | |
| 5 | 21 | |
| 6 | 12 | |
| 7 | A new 2D-HELS hyperchaotic map and its application on image encryption using RNA operation and dynamic confusionbreakdown → | 51 |
| 8 | 16 | |
| 9 | 46 | |
| 10 | 33 | |
| 11 | 11 | |
| 12 | 64 | |
| 13 | 6 | |
| 14 | 78 | |
| 15 | 8 | |
| 16 | Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learningbreakdown → | 350 |
| 17 | 17 | |
| 18 | Fully affine SAR image registration method based on feature points | 0 |
| 19 | 59 | |
| 20 | 2 |
About Xiaopeng Yan
Xiaopeng Yan is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 20 papers that have together received 825 indexed citations. Recurring topics across this work include Chaos-based Image/Signal Encryption (13 papers), Advanced Steganography and Watermarking Techniques (8 papers) and Cellular Automata and Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (651 citations), Artificial Intelligence (271 citations) and Mathematical Physics (66 citations). Xiaopeng Yan has collaborated with scholars based in China, Australia and Malaysia. Frequent co-authors include Yongjin Xian, Liang Lin, Xiaoxi Wang, Ziliang Chen, Xiaodan Liang, Xingyuan Wang, Lin Teng, Xingyuan Wang, Hao Wei and Mingxu Wang. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Information Sciences.
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.