Ming Dai

874 citations
47 papers · 556 · h-index 12

Impact in

    • Advanced Image and Video Retrieval Techniques
    • Advanced Neural Network Applications
    • Video Surveillance and Tracking Methods
    • Image Retrieval and Classification Techniques
    • Robotics and Sensor-Based Localization
    • Advanced Antenna and Metasurface Technologies

Papers in

Ming Dai

43 papers receiving 545 citations

Peers

Ming Dai
Comparison fields: 5 of 90
  • Computer Vision and Pattern Recognition 305
  • Aerospace Engineering 221
  • Media Technology 59
  • Geology 16
  • Electronic, Optical and Magnetic Materials 53
Replace Lai Kang with:
Lai Kang China
Yuyan Li China
Chang Nie China
Qingyu Hou China
Tao Fang China
Junwoo Park South Korea
Hong Ji China
Siqi Wang China
Huandong Chen China
Bohao Huang United States
Ming Dai relative to Lai Kang China Lai Kang's profile →
Citations per field
00.5×1.5×2.3×
Lai Kang · 1×
Citations per year

Countries citing papers authored by Ming Dai

Since Specialization
Citations

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

Fields of papers citing papers by Ming Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021133
2 201451
3 202149
4 202147
5 202334
6 202223
7 201718
8 201915
9 202213
10 202312
11 201912
12 201811
13 201011
14 202110
15 20189
16 20229
17 20208
18 20248
19 20228
20 20227

About Ming Dai

Ming Dai is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Astronomy and Astrophysics, Aerospace Engineering and Media Technology, having authored 47 papers that have together received 556 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (11 papers), Superconducting and THz Device Technology (6 papers), Medical Image Segmentation Techniques (5 papers), Advanced Vision and Imaging (5 papers), Robotics and Sensor-Based Localization (5 papers), Image Retrieval and Classification Techniques (4 papers), Image and Object Detection Techniques (3 papers) and Visual Attention and Saliency Detection (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (305 citations), Aerospace Engineering (221 citations), Media Technology (59 citations), Geology (16 citations) and Electronic, Optical and Magnetic Materials (53 citations). Ming Dai has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Jiedong Zhuang, Enhui Zheng, Ming Zhu, Liqiang Guo, Zuowan Zhou, Yifan Guo, Zhenyu Wang, Fuxi Peng, Zhiheng Zhou and Wankou Yang. Their work appears in journals such as Applied Physics Letters, Journal of Materials Science Materials in Electronics, Multimedia Tools and Applications, Materials Horizons and Journal of Low Temperature Physics.

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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