Soohwan Song

699 total citations
21 papers, 425 citations indexed

About

Soohwan Song is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, Soohwan Song has authored 21 papers receiving a total of 425 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 14 papers in Aerospace Engineering and 4 papers in Industrial and Manufacturing Engineering. Recurrent topics in Soohwan Song's work include Robotics and Sensor-Based Localization (14 papers), Robotic Path Planning Algorithms (7 papers) and Advanced Vision and Imaging (7 papers). Soohwan Song is often cited by papers focused on Robotics and Sensor-Based Localization (14 papers), Robotic Path Planning Algorithms (7 papers) and Advanced Vision and Imaging (7 papers). Soohwan Song collaborates with scholars based in South Korea, India and Canada. Soohwan Song's co-authors include Sungho Jo, Chang Sup Sung, Daekyum Kim, Sunghee Choi, M. Gerke, Mehdi Maboudi, M. Saadatseresht, Wonpil Yu and Jun‐Kyu Park and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Access and Pattern Recognition.

In The Last Decade

Soohwan Song

19 papers receiving 408 citations

Peers

Soohwan Song
Comparison fields: 5 of 49
  • Computer Vision and Pattern Recognition 251
  • Aerospace Engineering 229
  • Geology 89
  • Industrial and Manufacturing Engineering 88
  • Environmental Engineering 45
Replace Siavash Hosseinyalamdary with:
Siavash Hosseinyalamdary Netherlands
Héctor Azpúrua Brazil
S.N. Gottschlich United States
Tongyi Cao Hong Kong
Chenrui Wu China
Daniel Meyer-Delius Germany
Karl Tombre France
Ehsan Javanmardi Japan
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Citations per field, relative to Soohwan Song
Soohwan Song · 1×
Citations per year, relative to Soohwan Song
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Countries citing papers authored by Soohwan Song

Since Specialization
Citations

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

Fields of papers citing papers by Soohwan Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Soohwan Song

This figure shows the co-authorship network connecting the top 25 collaborators of Soohwan Song. A scholar is included among the top collaborators of Soohwan Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Soohwan Song. Soohwan Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 2
4 1
5 1
6 22
7 3
8 72
9 10
10 1
11 1
12 28
13 8
14 11
15 45
16 54
17 8
18 27
19 86
20 8

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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