Heping Song

404 citations
43 papers · 252 · h-index 9

Impact in

Papers in

Heping Song

37 papers receiving 244 citations

Peers

Heping Song
Comparison fields: 5 of 60
  • Signal Processing 60
  • Computer Vision and Pattern Recognition 109
  • Computational Mathematics 3
  • Artificial Intelligence 120
  • Software 13
Replace Xiaochen Lian with:
Xiaochen Lian China
Eleonora Grassucci Italy
Juhua Hu United States
Yuxin Zhang China
Hayato Kobayashi Japan
Benigno Uría United Kingdom
Onur Dikmen Finland
Kaoru Hiramatsu Japan
Mohamed R. Amer United States
S. Prasanna India
Heping Song relative to Xiaochen Lian China Xiaochen Lian's profile →
Citations per field
00.5×
Xiaochen Lian · 1×
Citations per year

Countries citing papers authored by Heping Song

Since Specialization
Citations

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

Fields of papers citing papers by Heping Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202043
2 202024
3 202322
4 201918
5 202313
6 202112
7 201111
8 20229
9 20209
10 20128
11 20218
12 20247
13 20225
14 20225
15 20185
16 20195
17 20114
18 20244
19 20234
20 20123

About Heping Song

Heping Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Signal Processing and Computational Mechanics, having authored 43 papers that have together received 252 indexed citations. Recurring topics across this work include Face and Expression Recognition (9 papers), Sparse and Compressive Sensing Techniques (6 papers), Advanced Image and Video Retrieval Techniques (5 papers), Photoacoustic and Ultrasonic Imaging (5 papers), Text and Document Classification Technologies (5 papers), Remote-Sensing Image Classification (4 papers), Bayesian Methods and Mixture Models (4 papers) and Microwave Imaging and Scattering Analysis (4 papers). The work is most often cited by research in Signal Processing (60 citations), Computer Vision and Pattern Recognition (109 citations), Computational Mathematics (3 citations), Artificial Intelligence (120 citations) and Software (13 citations). Heping Song has collaborated with scholars based in China, United Kingdom and Ghana. Frequent co-authors include Qirong Mao, Hongjie Jia, Liangjun Wang, Jianming Zhang, Chao Qi, Guoli Wang, Guoli Wang, Dongxia Zhu, Jun Liu and Jianping Gou. Their work appears in journals such as IEEE Access, International Journal of Intelligent Systems, Applied Sciences, Expert Systems with Applications and Neural Networks.

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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