Minye Wu

704 citations
29 papers · 445 · h-index 13

Impact in

Papers in

Minye Wu

27 papers receiving 424 citations

Peers

Minye Wu
Comparison fields: 5 of 56
  • Computer Graphics and Computer-Aided Design 225
  • Computer Vision and Pattern Recognition 358
  • Computational Mechanics 219
  • Human-Computer Interaction 39
  • Signal Processing 17
Replace Jason L. Mitchell with:
Jason L. Mitchell United States
Andreas Lehrmann Germany
Kripasindhu Sarkar Germany
Joerg H. Mueller Austria
Gernot Schaufler Austria
Shizhe Zhou China
Matthew Webb United Kingdom
Yuanqing Zhang China
M. Ikits United States
Jiapeng Tang China
Minye Wu relative to Jason L. Mitchell United States Jason L. Mitchell's profile →
Citations per field
00.5×10×17×
Jason L. Mitchell · 1×
Citations per year

Countries citing papers authored by Minye Wu

Since Specialization
Citations

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

Fields of papers citing papers by Minye Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202077
2 202259
3 202235
4 202134
5 202332
6 202226
7 201924
8 202119
9 202117
10 202115
11 202114
12 202114
13 202113
14 202011
15 202410
16 20218
17 20246
18 20186
19
Multiview Vehicle Tracking by Graph Matching Model.
20195
20 20205

About Minye Wu

Minye Wu is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics, Media Technology and Aerospace Engineering, having authored 29 papers that have together received 445 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (22 papers), Computer Graphics and Visualization Techniques (17 papers), 3D Shape Modeling and Analysis (12 papers), Human Pose and Action Recognition (6 papers), Video Surveillance and Tracking Methods (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Robotics and Sensor-Based Localization (2 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (225 citations), Computer Vision and Pattern Recognition (358 citations), Computational Mechanics (219 citations), Human-Computer Interaction (39 citations) and Signal Processing (17 citations). Minye Wu has collaborated with scholars based in China, Belgium and United Kingdom. Frequent co-authors include Jingyi Yu, Lan Xu, Yuehao Wang, Qiang Hu, Yuheng Jiang, Yingliang Zhang, Fuqiang Zhao, Kaiwen Guo, Ziyu Wang and Liao Wang. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Pattern Analysis and Machine Intelligence, Neurocomputing, IEEE Transactions on Visualization and Computer Graphics and IEEE Transactions on Image Processing.

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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