Woonhyun Nam
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence
- Automotive Engineering
- Aerospace Engineering
- Safety, Risk, Reliability and Quality top 10%
- Co-authors
- Joon Hee HanPiotr DollárBohyung HanSuha Kwak
- Topics
- Video Surveillance and Tracking Methods (8 papers)Advanced Vision and Imaging (5 papers)Advanced Image and Video Retrieval Techniques (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionAutomotive EngineeringSafety, Risk, Reliability and Quality
- Journals
- Pattern Recognition LettersIEEE Signal Processing LettersComputer Vision and Image Understanding
- Partner nations
- South KoreaUnited Kingdom
In The Last Decade
Woonhyun Nam
9 papers receiving 362 citations
Peers
Comparison fields: 5 of 32
- Computer Vision and Pattern Recognition 369
- Artificial Intelligence 60
- Automotive Engineering 50
- Aerospace Engineering 31
- Safety, Risk, Reliability and Quality 29
Countries citing papers authored by Woonhyun Nam
This map shows the geographic impact of Woonhyun Nam'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 Woonhyun Nam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Woonhyun Nam more than expected).
Fields of papers citing papers by Woonhyun Nam
This network shows the impact of papers produced by Woonhyun Nam. 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 Woonhyun Nam. The network helps show where Woonhyun Nam may publish in the future.
Co-authorship network of co-authors of Woonhyun Nam
This figure shows the co-authorship network connecting the top 25 collaborators of Woonhyun Nam. A scholar is included among the top collaborators of Woonhyun Nam 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 Woonhyun Nam. Woonhyun Nam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Local Decorrelation For Improved Pedestrian Detection | 238 |
| 2 | 2 | |
| 3 | 62 | |
| 4 | 48 | |
| 5 | 18 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | Individual Pedestrian Separation Using an EM Algorithm | 1 |
| 9 | 3 |
About Woonhyun Nam
Woonhyun Nam is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Building and Construction, having authored 9 papers that have together received 379 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (8 papers), Advanced Vision and Imaging (5 papers) and Advanced Image and Video Retrieval Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (369 citations), Automotive Engineering (50 citations) and Safety, Risk, Reliability and Quality (29 citations). Woonhyun Nam has collaborated with scholars based in South Korea and United Kingdom. Frequent co-authors include Joon Hee Han, Piotr Dollár, Bohyung Han and Suha Kwak. Their work appears in journals such as Pattern Recognition Letters, IEEE Signal Processing Letters and Computer Vision and Image Understanding.
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.