Jun Xiang

636 citations
47 papers · 401 · h-index 11

Impact in

Papers in

Jun Xiang

39 papers receiving 385 citations

Peers

Jun Xiang
Comparison fields: 5 of 64
  • Industrial and Manufacturing Engineering 204
  • Computer Vision and Pattern Recognition 266
  • Computer Graphics and Computer-Aided Design 28
  • Media Technology 46
  • Computational Mechanics 79
Replace Toshio Ueshiba with:
Toshio Ueshiba Japan
Yuying Ge Hong Kong
Andreas Doumanoglou United Kingdom
Chi-Ho Chan United Kingdom
Robert-Paul Berretty Netherlands
Alexandre Kaspar United States
Miklós Hoffmann Hungary
M. Masry United States
Ryosuke Furuta Japan
Jun Xiang relative to Toshio Ueshiba Japan Toshio Ueshiba's profile →
Citations per field
00.5×9.2×
Toshio Ueshiba · 1×
Citations per year

Countries citing papers authored by Jun Xiang

Since Specialization
Citations

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

Fields of papers citing papers by Jun Xiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Xiang, 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 Xiang Line = papers co-authored together Jun Xiang 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 202069
2 202263
3 201942
4 202028
5 202021
6 202420
7 201919
8 201919
9 202116
10 202212
11 201611
12 20217
13 20216
14 20225
15 20225
16 20195
17
Digital image compression based on BEMD and PCA
20114
18 20134
19 20224
20 20224

About Jun Xiang

Jun Xiang is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Media Technology, Polymers and Plastics and Computer Networks and Communications, having authored 47 papers that have together received 401 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (23 papers), Advanced Image and Video Retrieval Techniques (15 papers), Image Retrieval and Classification Techniques (13 papers), Textile materials and evaluations (6 papers), Image Enhancement Techniques (6 papers), Image Processing Techniques and Applications (4 papers), 3D Shape Modeling and Analysis (4 papers) and Generative Adversarial Networks and Image Synthesis (4 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (204 citations), Computer Vision and Pattern Recognition (266 citations), Computer Graphics and Computer-Aided Design (28 citations), Media Technology (46 citations) and Computational Mechanics (79 citations). Jun Xiang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Ruru Pan, Weidong Gao, Yudong Guo, Ning Zhang, Jingan Wang, Jian Zhou, H. C. Yang, Lei Wang, Juyong Zhang and Renzo Shamey. Their work appears in journals such as Textile Research Journal, Journal of the Textile Institute, IEEE Access, International Journal of Clothing Science and Technology and Fibres and Textiles in Eastern Europe.

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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