H. Yu
Impact in
-
- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Image Enhancement Techniques
- Image and Signal Denoising Methods
- Advanced Image and Video Retrieval Techniques
- Medical Image Segmentation Techniques
-
- Image Processing Techniques and Applications
Papers in
-
- Particle physics theoretical and experimental studies 3
- Neutrino Physics Research 2
- High-Energy Particle Collisions Research 2
- Astrophysics and Cosmic Phenomena 2
- Dark Matter and Cosmic Phenomena 1
- Co-authors
- Meifeng Lin (3 shared papers)D. Torbunov (3 shared papers)Yihui Ren (3 shared papers)Jin Huang (1 shared paper)Yi Huang (1 shared paper)Shinjae Yoo (1 shared paper)J. D. Osborn (1 shared paper)C. Pinkenburg (1 shared paper)
- Journals
- Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (1 paper)Physical review. C (1 paper)Machine Learning Science and Technology (1 paper)Journal of Instrumentation (1 paper)2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)
- Partner nations
- United StatesItalySwitzerland
In The Last Decade
H. Yu
3 papers receiving 113 citations
Peers
Comparison fields: 5 of 47
- Computer Vision and Pattern Recognition 70
- Media Technology 15
- Health Informatics 2
- Computer Graphics and Computer-Aided Design 5
- Biophysics 6
Countries citing papers authored by H. Yu
This map shows the geographic impact of H. Yu'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 H. Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites H. Yu more than expected).
Fields of papers citing papers by H. Yu
This network shows the impact of papers produced by H. Yu. 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 H. Yu. The network helps show where H. Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside H. Yu, 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 | 2023 | 105 | |
| 2 | 2021 | 6 | |
| 3 | 2024 | 4 | |
| 4 | 2021 | 0 | |
| 5 | 2023 | 0 | |
| 6 | 2024 | 0 | |
| 7 | 2025 | 0 |
About H. Yu
H. Yu is a scholar working on Nuclear and High Energy Physics, Computer Vision and Pattern Recognition, Endocrine and Autonomic Systems, Artificial Intelligence and Media Technology, having authored 7 papers that have together received 115 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (3 papers), Neutrino Physics Research (2 papers), High-Energy Particle Collisions Research (2 papers), Astrophysics and Cosmic Phenomena (2 papers), Adipose Tissue and Metabolism (1 paper), Circadian rhythm and melatonin (1 paper), Eicosanoids and Hypertension Pharmacology (1 paper) and Dark Matter and Cosmic Phenomena (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (70 citations), Media Technology (15 citations), Health Informatics (2 citations), Computer Graphics and Computer-Aided Design (5 citations) and Biophysics (6 citations). H. Yu has collaborated with scholars based in United States, Italy and Switzerland. Frequent co-authors include Meifeng Lin, D. Torbunov, Yihui Ren, Jin Huang, Yi Huang, Shinjae Yoo, J. D. Osborn, C. Pinkenburg, C. Roland and A. D. Frawley. Their work appears in journals such as Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Physical review. C, Machine Learning Science and Technology, Journal of Instrumentation and 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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.