M.-T. Sun

1.2k citations
25 papers · 970 · h-index 13

Impact in

Papers in

M.-T. Sun

22 papers receiving 882 citations

Peers

M.-T. Sun
Comparison fields: 5 of 42
  • Signal Processing 721
  • Computer Vision and Pattern Recognition 751
  • Hardware and Architecture 86
  • Computational Theory and Mathematics 81
  • Artificial Intelligence 139
Replace Jer Min Jou with:
Jer Min Jou Taiwan
Tenkasi V. Ramabadran United States
I. Kuroda Japan
Byeong Lee South Korea
E.V. Jones United Kingdom
Jau‐Yien Lee Taiwan
Antti Hallapuro Finland
Mahesh Mehendale India
S. Panchanathan Canada
Javier Hormigo Spain
M.-T. Sun relative to Jer Min Jou Taiwan Jer Min Jou's profile →
Citations per field
00.5×2×4×5.3×
Jer Min Jou · 1×
Citations per year

Countries citing papers authored by M.-T. Sun

Since Specialization
Citations

This map shows the geographic impact of M.-T. Sun'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 M.-T. Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M.-T. Sun more than expected).

Fields of papers citing papers by M.-T. Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M.-T. Sun. 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 M.-T. Sun. The network helps show where M.-T. Sun may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1989267
2 1989172
3 1991149
4 198772
5 200161
6 199244
7 200536
8 200132
9 200129
10 199218
11 199816
12 198713
13 199113
14
Special Issue on Segmentation, Description and Retrieval of Video Content
199810
15 200210
16 20037
17 20045
18 19995
19 20034
20 20114

About M.-T. Sun

M.-T. Sun is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Biomedical Engineering and Computational Theory and Mathematics, having authored 25 papers that have together received 970 indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (8 papers), Digital Filter Design and Implementation (7 papers), Advanced Data Compression Techniques (7 papers), Analog and Mixed-Signal Circuit Design (5 papers), Algorithms and Data Compression (4 papers), Advanced Vision and Imaging (3 papers), Image and Video Quality Assessment (3 papers) and Multimedia Communication and Technology (2 papers). The work is most often cited by research in Signal Processing (721 citations), Computer Vision and Pattern Recognition (751 citations), Hardware and Architecture (86 citations), Computational Theory and Mathematics (81 citations) and Artificial Intelligence (139 citations). M.-T. Sun has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Long Wu, K.-M. Yang, Shawmin Lei, M.L. Liou, Supavadee Aramvith, Daniel Gática-Pérez, Salvador Ruíz-Correa, Kou-Hu Tzou, Masashi Maruyama and Hiroshi Fujiwara. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, ACM Transactions on Multimedia Computing Communications and Applications, IEEE Transactions on Image Processing, Proceedings of the IEEE and IEEE Transactions on Circuits and Systems.

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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