Ming Dai

573 total citations
32 papers, 357 citations indexed

About

Ming Dai is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering. According to data from OpenAlex, Ming Dai has authored 32 papers receiving a total of 357 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 5 papers in Media Technology and 5 papers in Aerospace Engineering. Recurrent topics in Ming Dai's work include Advanced Image and Video Retrieval Techniques (11 papers), Medical Image Segmentation Techniques (5 papers) and Advanced Vision and Imaging (5 papers). Ming Dai is often cited by papers focused on Advanced Image and Video Retrieval Techniques (11 papers), Medical Image Segmentation Techniques (5 papers) and Advanced Vision and Imaging (5 papers). Ming Dai collaborates with scholars based in China, United States and United Kingdom. Ming Dai's co-authors include Enhui Zheng, Liqiang Guo, Ming Zhu, Zhiheng Zhou, Wankou Yang, Zhenhua Feng, Lei Qi, Xiangwei Li, Tianlei Wang and Xiaoqiang Ji and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Access and Pattern Recognition.

In The Last Decade

Ming Dai

27 papers receiving 351 citations

Peers

Ming Dai
Comparison fields: 5 of 64
  • Computer Vision and Pattern Recognition 292
  • Aerospace Engineering 170
  • Media Technology 58
  • Artificial Intelligence 25
  • Ocean Engineering 20
Replace Zonghao Guo with:
Zonghao Guo China
Sixing Hu Singapore
Fabio Bellavia Italy
Menghua Zhai United States
Famao Ye China
Luc Courtrai France
Cyril Meurie France
Georges Baatz Switzerland
Zonghao Guo China View profile →
Citations per field, relative to Ming Dai
Ming Dai · 1×
Citations per year, relative to Ming Dai
Ming Dai · 1×

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-authorship network of co-authors of Ming Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Dai. A scholar is included among the top collaborators of Ming Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming Dai. Ming Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 4
4 7
5 1
6 2
7 0
8 29
9 120
10 10
11 2
12 47
13 2
14 3
15 2
16
LVQ neural network approach for fault location of distribution network
0
17 2
18
[Ecological characteristics of phytoplankton in coastal area of Pearl River estuary].
6
19 3
20 3

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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