Cho-Ying Wu
Impact in
- Geology top 5%
- 3D Surveying and Cultural Heritage
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- Computer Graphics and Visualization Techniques
Papers in
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- Advanced Vision and Imaging 4
- Face recognition and analysis 3
- Advanced Image Processing Techniques 3
- Image Enhancement Techniques 2
- Face and Expression Recognition 2
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- Sparse and Compressive Sensing Techniques 2
- 3D Shape Modeling and Analysis 2
- Co-authors
- Ulrich Neumann (7 shared papers)Qiangeng Xu (2 shared papers)Xudong Sun (1 shared paper)Jialiang Wang (1 shared paper)Shuochen Su (1 shared paper)C. Michael Hall (1 shared paper)Chin-Cheng Hsu (1 shared paper)Jian–Jiun Ding (2 shared papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesTaiwan
In The Last Decade
Cho-Ying Wu
8 papers receiving 280 citations
Peers
Comparison fields: 5 of 48
- Geology 97
- Computer Graphics and Computer-Aided Design 45
- Computer Vision and Pattern Recognition 158
- Computational Mechanics 142
- Environmental Engineering 70
Countries citing papers authored by Cho-Ying Wu
This map shows the geographic impact of Cho-Ying Wu'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 Cho-Ying Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cho-Ying Wu more than expected).
Fields of papers citing papers by Cho-Ying Wu
This network shows the impact of papers produced by Cho-Ying Wu. 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 Cho-Ying Wu. The network helps show where Cho-Ying Wu may publish in the future.
Co-authors
The 10 scholars most cited alongside Cho-Ying Wu, 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 | 2020 | 175 | |
| 2 | 2022 | 44 | |
| 3 | 2021 | 42 | |
| 4 | 2022 | 9 | |
| 5 | Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion | 2019 | 5 |
| 6 | 2016 | 5 | |
| 7 | 2021 | 2 | |
| 8 | 2017 | 1 | |
| 9 | 2019 | 0 |
About Cho-Ying Wu
Cho-Ying Wu is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Environmental Engineering, Dermatology and Media Technology, having authored 9 papers that have together received 283 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (4 papers), Face recognition and analysis (3 papers), Advanced Image Processing Techniques (3 papers), Image Enhancement Techniques (2 papers), Remote Sensing and LiDAR Applications (2 papers), Face and Expression Recognition (2 papers), Sparse and Compressive Sensing Techniques (2 papers) and 3D Shape Modeling and Analysis (2 papers). The work is most often cited by research in Geology (97 citations), Computer Graphics and Computer-Aided Design (45 citations), Computer Vision and Pattern Recognition (158 citations), Computational Mechanics (142 citations) and Environmental Engineering (70 citations). Cho-Ying Wu has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Ulrich Neumann, Qiangeng Xu, Xudong Sun, Jialiang Wang, Shuochen Su, C. Michael Hall, Chin-Cheng Hsu, Jian–Jiun Ding, Suya You and Yiqi Zhong. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
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.