Guangming Shi
- Computer Vision and Pattern Recognition top 2%
- Media Technology top 2%
- Health, Toxicology and Mutagenesis top 5%
- Atmospheric Science
- Environmental Engineering top 10%
- Topics
- Air Quality and Health Impacts (5 papers)Atmospheric chemistry and aerosols (5 papers)Image and Signal Denoising Methods (5 papers)
- Journals
- The Science of The Total EnvironmentScientific ReportsIEEE Transactions on Image Processing
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Guangming Shi
21 papers receiving 602 citations
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 404
- Media Technology 196
- Health, Toxicology and Mutagenesis 145
- Atmospheric Science 98
- Environmental Engineering 71
Countries citing papers authored by Guangming Shi
This map shows the geographic impact of Guangming Shi'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 Guangming Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guangming Shi more than expected).
Fields of papers citing papers by Guangming Shi
This network shows the impact of papers produced by Guangming Shi. 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 Guangming Shi. The network helps show where Guangming Shi may publish in the future.
Co-authorship network of co-authors of Guangming Shi
This figure shows the co-authorship network connecting the top 25 collaborators of Guangming Shi. A scholar is included among the top collaborators of Guangming Shi 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 Guangming Shi. Guangming Shi 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 | 24 | |
| 3 | 26 | |
| 4 | 34 | |
| 5 | 31 | |
| 6 | 9 | |
| 7 | 19 | |
| 8 | 54 | |
| 9 | Pattern Recognition and Computer Vision Second Chinese Conference, PRCV 2019, Xi’an, China, November 8–11, 2019, Proceedings, Part III | 2 |
| 10 | 1 | |
| 11 | 36 | |
| 12 | 216 | |
| 13 | 15 | |
| 14 | 107 | |
| 15 | 9 | |
| 16 | 1 | |
| 17 | 4 | |
| 18 | 9 | |
| 19 | 5 | |
| 20 | 2 |
About Guangming Shi
Guangming Shi is a scholar working on Computer Vision and Pattern Recognition, Health, Toxicology and Mutagenesis and Media Technology, having authored 21 papers that have together received 616 indexed citations. Recurring topics across this work include Air Quality and Health Impacts (5 papers), Atmospheric chemistry and aerosols (5 papers) and Image and Signal Denoising Methods (5 papers). The work is most often cited by research in Media Technology (196 citations), Computer Vision and Pattern Recognition (404 citations) and Health, Toxicology and Mutagenesis (145 citations). Guangming Shi has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Jinjian Wu, Anmin Liu, Weisi Lin, Weisheng Dong, Xiaolin Wu, Lei Zhang, Fumo Yang, Liangjun Wang, Xiaolin Wu and Leiming Zhang. Their work appears in journals such as The Science of The Total Environment, Scientific Reports and IEEE Transactions on Image Processing.
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.