Ginmo Chung
Impact in
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- Medical Image Segmentation Techniques
- Image and Signal Denoising Methods
- Advanced Image Processing Techniques
- Image and Object Detection Techniques
- Media Technology top 10%
- Advanced Image Fusion Techniques
Papers in
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- Medical Image Segmentation Techniques 4
- Image and Signal Denoising Methods 2
- Generative Adversarial Networks and Image Synthesis 1
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- Enhanced Oil Recovery Techniques 1
- Co-authors
- Jooyoung Hahn (1 shared paper)Xue‐Cheng Tai (1 shared paper)Luminita A. Vese (3 shared papers)Alan Yuille (1 shared paper)Miyoun Jung (1 shared paper)Ganesh Sundaramoorthi (1 shared paper)Nicolay M. Tanushev (1 shared paper)Triet Le (1 shared paper)
- Journals
- SIAM Journal on Imaging Sciences (1 paper)Computing and Visualization in Science (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2 papers)
- Partner nations
- United States
In The Last Decade
Ginmo Chung
4 papers receiving 197 citations
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 175
- Media Technology 34
- Computational Mechanics 75
- Mathematical Physics 24
- Computer Graphics and Computer-Aided Design 9
Countries citing papers authored by Ginmo Chung
This map shows the geographic impact of Ginmo Chung'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 Ginmo Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ginmo Chung more than expected).
Fields of papers citing papers by Ginmo Chung
This network shows the impact of papers produced by Ginmo Chung. 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 Ginmo Chung. The network helps show where Ginmo Chung may publish in the future.
Co-authors
The 8 scholars most cited alongside Ginmo Chung, 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 | 2011 | 135 | |
| 2 | 2008 | 60 | |
| 3 | 2009 | 18 | |
| 4 | 2006 | 5 |
About Ginmo Chung
Ginmo Chung is a scholar working on Computer Vision and Pattern Recognition, Ocean Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging and Biophysics, having authored 4 papers that have together received 218 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (4 papers), Image and Signal Denoising Methods (2 papers), Sparse and Compressive Sensing Techniques (1 paper), Advanced X-ray and CT Imaging (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Medical Imaging Techniques and Applications (1 paper), Cell Image Analysis Techniques (1 paper) and Enhanced Oil Recovery Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (175 citations), Media Technology (34 citations), Computational Mechanics (75 citations), Mathematical Physics (24 citations) and Computer Graphics and Computer-Aided Design (9 citations). Ginmo Chung has collaborated with scholars based in United States. Frequent co-authors include Jooyoung Hahn, Xue‐Cheng Tai, Luminita A. Vese, Alan Yuille, Miyoun Jung, Ganesh Sundaramoorthi, Nicolay M. Tanushev and Triet Le. Their work appears in journals such as SIAM Journal on Imaging Sciences, Computing and Visualization in Science and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.