Mun-Cheon Kang

618 citations
19 papers · 229 · h-index 8

Impact in

Papers in

Mun-Cheon Kang

19 papers receiving 216 citations

Peers

Mun-Cheon Kang
Comparison fields: 5 of 58
  • Human-Computer Interaction 46
  • Computer Vision and Pattern Recognition 137
  • Media Technology 35
  • Cognitive Neuroscience 72
  • Aerospace Engineering 27
Replace Vicky Kalogeiton with:
Vicky Kalogeiton France
Jee-Young Sun South Korea
M. Masry United States
Elisa Sayrol Spain
Dan Su China
Christopher Jaynes United States
Anlin Zheng China
Shinji Mizuno Japan
Stefan Maierhofer Austria
Shuhan Chen China
Mun-Cheon Kang relative to Vicky Kalogeiton France Vicky Kalogeiton's profile →
Citations per field
00.5×
Vicky Kalogeiton · 1×
Citations per year

Countries citing papers authored by Mun-Cheon Kang

Since Specialization
Citations

This map shows the geographic impact of Mun-Cheon Kang'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 Mun-Cheon Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mun-Cheon Kang more than expected).

Fields of papers citing papers by Mun-Cheon Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mun-Cheon Kang. 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 Mun-Cheon Kang. The network helps show where Mun-Cheon Kang may publish in the future.

Co-authors

The 9 scholars most cited alongside Mun-Cheon Kang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mun-Cheon Kang Line = papers co-authored together Mun-Cheon Kang links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201535
2 201432
3 201330
4 201729
5 201820
6 201719
7 201614
8 20177
9 20206
10 20186
11 20146
12 20185
13 20155
14 20144
15 20183
16 20173
17 20142
18 20162
19 20161

About Mun-Cheon Kang

Mun-Cheon Kang is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Cognitive Neuroscience, Media Technology and Aerospace Engineering, having authored 19 papers that have together received 229 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (6 papers), Gaze Tracking and Assistive Technology (6 papers), Tactile and Sensory Interactions (4 papers), Image Enhancement Techniques (3 papers), Visual Attention and Saliency Detection (3 papers), Robotics and Sensor-Based Localization (3 papers), Medical Image Segmentation Techniques (2 papers) and Glaucoma and retinal disorders (2 papers). The work is most often cited by research in Human-Computer Interaction (46 citations), Computer Vision and Pattern Recognition (137 citations), Media Technology (35 citations), Cognitive Neuroscience (72 citations) and Aerospace Engineering (27 citations). Mun-Cheon Kang has collaborated with scholars based in South Korea and United States. Frequent co-authors include Sung-Jea Ko, Jee-Young Sun, Dongni Zhang, Jinwoo Yoo, Jong‐Woo Han, Seung‐Wook Kim, Seung-Jun Lee, Dae-Hwan Kim and Yong-Goo Shin. Their work appears in journals such as IEEE Transactions on Consumer Electronics, IEEE Access, Optical Engineering, Electronics Letters and Signal Processing Image Communication.

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.

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