In Seop Na
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- Image Retrieval and Classification Techniques 23
- Handwritten Text Recognition Techniques 18
- Advanced Image and Video Retrieval Techniques 15
- Face and Expression Recognition 8
- Face recognition and analysis 7
- Video Surveillance and Tracking Methods 7
- Image Processing and 3D Reconstruction 6
- Human-Computer Interaction top 10%
- Media Technology top 10%
- Analytical Chemistry top 10%
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- Smart Agriculture and AI 9
- Co-authors
- Soo-Hyung KimHyung-Jeong YangGuee-Sang LeeTuan Anh TranMd Nasim RezaKyeong–Hwan LeeMyung‐Sun KimSun‐Hee Kim
- Cited by
- Computer Vision and Pattern RecognitionExperimental and Cognitive PsychologyHuman-Computer Interaction
- Journals
- Expert Systems with Applications (1 paper)IEEE Access (1 paper)Computers and Electronics in Agriculture (1 paper)
- Partner nations
- South KoreaVietnamUnited States
In The Last Decade
In Seop Na
75 papers receiving 633 citations
Peers
Comparison fields: 5 of 103
- Computer Vision and Pattern Recognition 347
- Experimental and Cognitive Psychology 101
- Human-Computer Interaction 39
- Media Technology 52
- Analytical Chemistry 41
Countries citing papers authored by In Seop Na
This map shows the geographic impact of In Seop Na'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 In Seop Na with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites In Seop Na more than expected).
Fields of papers citing papers by In Seop Na
This network shows the impact of papers produced by In Seop Na. 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 In Seop Na. The network helps show where In Seop Na may publish in the future.
Co-authorship network
The 25 scholars most cited alongside In Seop Na, 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 | 2025 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 5 | |
| 8 | 2022 | 19 | |
| 9 | 2022 | 1 | |
| 10 | 2021 | 16 | |
| 11 | 2021 | 5 | |
| 12 | 2019 | 2 | |
| 13 | 2019 | 50 | |
| 14 | 2018 | 13 | |
| 15 | 2018 | 110 | |
| 16 | 2018 | 2 | |
| 17 | 2017 | 2 | |
| 18 | 2016 | 12 | |
| 19 | 2016 | 2 | |
| 20 | An Efficient Non-Maximum Suppression for Pedestrian Detection Using Mean-Shift Algorithm and Linear SVM Classifier | 2014 | 2 |
About In Seop Na
In Seop Na is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Signal Processing, having authored 83 papers that have together received 667 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (23 papers), Handwritten Text Recognition Techniques (18 papers), Advanced Image and Video Retrieval Techniques (15 papers), Smart Agriculture and AI (9 papers), Face and Expression Recognition (8 papers), Face recognition and analysis (7 papers), Video Surveillance and Tracking Methods (7 papers) and Image Processing and 3D Reconstruction (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (347 citations), Experimental and Cognitive Psychology (101 citations) and Human-Computer Interaction (39 citations). In Seop Na has collaborated with scholars based in South Korea, Vietnam and United States. Frequent co-authors include Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, Tuan Anh Tran, Md Nasim Reza, Kyeong–Hwan Lee, Myung‐Sun Kim, Sun‐Hee Kim, Soonja Yeom and Hoon Ko. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Computers and Electronics in Agriculture.
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.