Jun Xin

2.6k citations
125 papers · 1.9k · h-index 24

Impact in

Papers in

Jun Xin

118 papers receiving 1.9k citations

Peers

Jun Xin
Comparison fields: 5 of 120
  • Signal Processing 556
  • Computer Vision and Pattern Recognition 604
  • Obstetrics and Gynecology 120
  • Radiology, Nuclear Medicine and Imaging 301
  • Fluid Flow and Transfer Processes 59
Replace Shin-ichiro Umemura with:
Shin-ichiro Umemura Japan
Ali Khamene United States
Soo‐Yong Lee South Korea
Tong San Koh Singapore
Banghe Zhu United States
Tong Ding China
Kyunghyun Sung United States
Junyu Wei China
Tsuyoshi Shiina Japan
Israel Gannot Israel
Jun Xin relative to Shin-ichiro Umemura Japan Shin-ichiro Umemura's profile →
Citations per field
00.5×10×20×29.5×
Shin-ichiro Umemura · 1×
Citations per year

Countries citing papers authored by Jun Xin

Since Specialization
Citations

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

Fields of papers citing papers by Jun Xin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 125 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004214
2 2009161
3
View Synthesis for Multiview Video Compression
200676
4 200869
5
Coding Approaches for End-To-End 3D TV Systems
200455
6 200650
7 201843
8 201743
9 200841
10 200941
11 202040
12 200538
13 200735
14 201933
15 199933
16 201431
17 200731
18 200531
19 200828
20 201626

About Jun Xin

Jun Xin is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Signal Processing, Materials Chemistry and Electrical and Electronic Engineering, having authored 125 papers that have together received 1.9k indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (22 papers), Radiomics and Machine Learning in Medical Imaging (18 papers), Medical Imaging Techniques and Applications (14 papers), Advanced Vision and Imaging (13 papers), Silicon Carbide Semiconductor Technologies (12 papers), Acoustic Wave Resonator Technologies (12 papers), Advanced Data Compression Techniques (11 papers) and MRI in cancer diagnosis (11 papers). The work is most often cited by research in Signal Processing (556 citations), Computer Vision and Pattern Recognition (604 citations), Obstetrics and Gynecology (120 citations), Radiology, Nuclear Medicine and Imaging (301 citations) and Fluid Flow and Transfer Processes (59 citations). Jun Xin has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Ming–Ting Sun, Anthony Vetro, Yanqing Zheng, Chia‐Wen Lin, Haikuan Kong, Er‐Wei Shi, Huifang Sun, Hongzan Sun, Thomas R. Shrout and Shujun Zhang. Their work appears in journals such as Nuclear Medicine Communications, Medicine, European Journal of Radiology, SAE technical papers on CD-ROM/SAE technical paper series and International Journal of Heat and Fluid Flow.

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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