Song De
Impact in
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- Computer Graphics and Visualization Techniques
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- Advanced Vision and Imaging
- Optical measurement and interference techniques
- Image and Object Detection Techniques
Papers in
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- Advanced Vision and Imaging 11
- Image and Object Detection Techniques 8
- Medical Image Segmentation Techniques 4
- Image Retrieval and Classification Techniques 4
- Optical measurement and interference techniques 3
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- Advanced Numerical Analysis Techniques 4
- Co-authors
- Guo-Qing Wei (1 shared paper)André Gagalowicz (5 shared papers)Hong Lin (1 shared paper)Bingcheng Li (1 shared paper)Zhanyi Hu (2 shared papers)Qifa Ke (2 shared papers)Tianzi Jiang (2 shared papers)Chang Eun Song (1 shared paper)
In The Last Decade
Song De
28 papers receiving 495 citations
Peers
Comparison fields: 5 of 63
- Computer Graphics and Computer-Aided Design 80
- Computer Vision and Pattern Recognition 421
- Geology 67
- Media Technology 97
- Computational Mechanics 88
Countries citing papers authored by Song De
This map shows the geographic impact of Song De'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 Song De with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song De more than expected).
Fields of papers citing papers by Song De
This network shows the impact of papers produced by Song De. 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 Song De. The network helps show where Song De may publish in the future.
Co-authors
The 13 scholars most cited alongside Song De, 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 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1994 | 219 | |
| 2 | 1993 | 85 | |
| 3 | 1985 | 53 | |
| 4 | 1988 | 38 | |
| 5 | 1986 | 24 | |
| 6 | 1997 | 12 | |
| 7 | 1995 | 11 | |
| 8 | 2002 | 11 | |
| 9 | 2002 | 9 | |
| 10 | 1996 | 8 | |
| 11 | 2002 | 8 | |
| 12 | 2020 | 8 | |
| 13 | 2022 | 7 | |
| 14 | 2020 | 7 | |
| 15 | 1997 | 7 | |
| 16 | 1986 | 6 | |
| 17 | 1999 | 5 | |
| 18 | 1999 | 4 | |
| 19 | 1985 | 4 | |
| 20 | 2002 | 3 |
About Song De
Song De is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Computer Graphics and Computer-Aided Design, Aerospace Engineering and Media Technology, having authored 31 papers that have together received 542 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (11 papers), Image and Object Detection Techniques (8 papers), Computer Graphics and Visualization Techniques (5 papers), Medical Image Segmentation Techniques (4 papers), Image Retrieval and Classification Techniques (4 papers), Advanced Numerical Analysis Techniques (4 papers), Robotics and Sensor-Based Localization (4 papers) and Optical measurement and interference techniques (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (80 citations), Computer Vision and Pattern Recognition (421 citations), Geology (67 citations), Media Technology (97 citations) and Computational Mechanics (88 citations). Song De has collaborated with scholars based in China, France and Japan. Frequent co-authors include Guo-Qing Wei, André Gagalowicz, Hong Lin, Bingcheng Li, Zhanyi Hu, Qifa Ke, Tianzi Jiang, Chang Eun Song, Li Li and Hao Zhang. Their work appears in journals such as Pattern Recognition, Computers & Graphics, IEEE photonics journal, Pattern Recognition Letters and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.