Jun Kong

1.7k citations
120 papers · 1.2k · h-index 20

Impact in

Papers in

Jun Kong

111 papers receiving 1.2k citations

Peers

Jun Kong
Comparison fields: 5 of 113
  • Computer Vision and Pattern Recognition 855
  • Media Technology 165
  • Human-Computer Interaction 91
  • Artificial Intelligence 344
  • Biomedical Engineering 269
Replace Dipti Prasad Mukherjee with:
Dipti Prasad Mukherjee India
Jie Nie China
Snehasis Mukherjee India
Yong Zhao China
Huiyuan Fu China
Chirag Patel India
Junqing Yu China
Sergey Ablameyko Belarus
Ross Goroshin United States
Xiangtai Li China
Jun Kong relative to Dipti Prasad Mukherjee India Dipti Prasad Mukherjee's profile →
Citations per field
00.5×3.3×
Dipti Prasad Mukherjee · 1×
Citations per year

Countries citing papers authored by Jun Kong

Since Specialization
Citations

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

Fields of papers citing papers by Jun Kong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Kong, 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 Kong Line = papers co-authored together Jun Kong links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201784
2 201263
3 201559
4 202250
5 202148
6
Multi-focus Image Fusion Using Spatial Frequency and Genetic Algorithm
200838
7
Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.
201936
8 201935
9 202034
10 202230
11 202229
12 202125
13 201825
14 202124
15 202224
16 201424
17 201824
18 202122
19 202321
20 202320

About Jun Kong

Jun Kong is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Artificial Intelligence, Media Technology and Aerospace Engineering, having authored 120 papers that have together received 1.2k indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (45 papers), Human Pose and Action Recognition (37 papers), Gait Recognition and Analysis (31 papers), Anomaly Detection Techniques and Applications (19 papers), Face recognition and analysis (14 papers), Face and Expression Recognition (11 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Advanced Image Fusion Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (855 citations), Media Technology (165 citations), Human-Computer Interaction (91 citations), Artificial Intelligence (344 citations) and Biomedical Engineering (269 citations). Jun Kong has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Min Jiang, Tianshan Liu, Jianzhong Wang, Baoxue Zhang, Hongtao Huo, Kin‐Man Lam, Miao Qi, Yinghua Lu, Shengwei Tian and George Bebis. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, IEEE Signal Processing Letters, Journal of Visual Communication and Image Representation, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions on Multimedia.

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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