Jun-Mo Kim

483 citations
17 papers · 287 · h-index 6

Impact in

    • Medical Image Segmentation Techniques
    • Image and Object Detection Techniques
    • Image Retrieval and Classification Techniques
    • Image and Signal Denoising Methods
    • Advanced Image and Video Retrieval Techniques
    • Advanced Vision and Imaging

Papers in

Jun-Mo Kim

13 papers receiving 279 citations

Peers

Jun-Mo Kim
Comparison fields: 5 of 65
  • Computer Vision and Pattern Recognition 180
  • Media Technology 29
  • Biophysics 16
  • Cognitive Neuroscience 51
  • Computational Mathematics 1
Replace S. N. Kumar with:
S. N. Kumar India
Noura A. Semary Egypt
Tessamma Thomas India
Dmitry Laptev Switzerland
Jundong Liu United States
Said Ghoniemy Egypt
Xufeng Yao China
Neelam Sinha India
Sawon Pratiher India
Delphine Nain United States
Jun-Mo Kim relative to S. N. Kumar India S. N. Kumar's profile →
Citations per field
00.5×4.5×
S. N. Kumar · 1×
Citations per year

Countries citing papers authored by Jun-Mo Kim

Since Specialization
Citations

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

Fields of papers citing papers by Jun-Mo Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun-Mo Kim. 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 Jun-Mo Kim. The network helps show where Jun-Mo Kim may publish in the future.

Co-authors

The 23 scholars most cited alongside Jun-Mo Kim, 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 Jun-Mo Kim Line = papers co-authored together Jun-Mo Kim links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 2005205
2 202114
3 202414
4 202312
5 202311
6 202411
7 20245
8 20234
9 20244
10
Comparison of the Results between Heidelberg Retina Tomography II and Stratus Optical Coherence Tomography in Glaucoma
20063
11 20242
12 20251
13 20231
14 20250
15 20240
16 20220
17 20250

About Jun-Mo Kim

Jun-Mo Kim is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience, Cardiology and Cardiovascular Medicine, Artificial Intelligence and Electrical and Electronic Engineering, having authored 17 papers that have together received 287 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (11 papers), Neuroscience and Neural Engineering (3 papers), Advanced Memory and Neural Computing (3 papers), ECG Monitoring and Analysis (3 papers), Neural Networks and Applications (2 papers), Hand Gesture Recognition Systems (1 paper), Retinal Diseases and Treatments (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (180 citations), Media Technology (29 citations), Biophysics (16 citations), Cognitive Neuroscience (51 citations) and Computational Mathematics (1 citation). Jun-Mo Kim has collaborated with scholars based in South Korea, United States and Türkiye. Frequent co-authors include Anthony Yezzi, Müjdat Çetin, John W. Fisher, A. S. Willsky, Tae‐Eui Kam, Dong-Ok Won, Cheolsoo Park, Ji-Hoon Jeong, Ko Keun Kim and Weili Lin. Their work appears in journals such as Expert Systems with Applications, IEEE Transactions on Systems Man and Cybernetics Systems, IEEE Transactions on Image Processing, Sensors and Frontiers in Human Neuroscience.

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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