Kang-Yu Ni
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics
- Artificial Intelligence
- Biomedical Engineering
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Tony F. ChanSelim EsedoḡluXavier BressonByung‐Woo HongStefano SoattoSvetlana RoudenkoDouglas CochranP. Mahanti
- Topics
- Sparse and Compressive Sensing Techniques (7 papers)Medical Image Segmentation Techniques (4 papers)Image and Signal Denoising Methods (3 papers)
- Journals
- International Journal of Computer VisionComputer Vision and Image UnderstandingLecture notes in computer science
- Partner nations
- United StatesSouth KoreaChina
In The Last Decade
Kang-Yu Ni
17 papers receiving 229 citations
Peers
Comparison fields: 5 of 66
- Computer Vision and Pattern Recognition 165
- Computational Mechanics 47
- Artificial Intelligence 26
- Biomedical Engineering 24
- Radiology, Nuclear Medicine and Imaging 23
Countries citing papers authored by Kang-Yu Ni
This map shows the geographic impact of Kang-Yu Ni'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 Kang-Yu Ni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kang-Yu Ni more than expected).
Fields of papers citing papers by Kang-Yu Ni
This network shows the impact of papers produced by Kang-Yu Ni. 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 Kang-Yu Ni. The network helps show where Kang-Yu Ni may publish in the future.
Co-authorship network of co-authors of Kang-Yu Ni
This figure shows the co-authorship network connecting the top 25 collaborators of Kang-Yu Ni. A scholar is included among the top collaborators of Kang-Yu Ni 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 Kang-Yu Ni. Kang-Yu Ni 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 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 0 | |
| 10 | 4 | |
| 11 | 10 | |
| 12 | 5 | |
| 13 | 9 | |
| 14 | 9 | |
| 15 | 147 | |
| 16 | 13 | |
| 17 | Variational pde-based image segmentation and inpainting with applications in computer graphics | 2 |
| 18 | Histogram based segmentation using Wasserstein distances | 22 |
| 19 | Matting through variational inpainting | 2 |
| 20 | 2 |
About Kang-Yu Ni
Kang-Yu Ni is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 20 papers that have together received 240 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (7 papers), Medical Image Segmentation Techniques (4 papers) and Image and Signal Denoising Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (165 citations), Computational Mechanics (47 citations) and Acoustics and Ultrasonics (2 citations). Kang-Yu Ni has collaborated with scholars based in United States, South Korea and China. Frequent co-authors include Tony F. Chan, Selim Esedoḡlu, Xavier Bresson, Byung‐Woo Hong, Stefano Soatto, Svetlana Roudenko, Douglas Cochran, P. Mahanti, Tsai-Ching Lu and Shankar Rao. Their work appears in journals such as International Journal of Computer Vision, Computer Vision and Image Understanding and Lecture notes in computer science.
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.