Anbumani Subramanian

765 citations
21 papers · 390 indexed · h-index 9

Anbumani Subramanian

19 papers receiving 371 citations

Peers

Anbumani Subramanian
Comparison fields: 5 of 63
  • Human-Computer Interaction 81
  • Computer Vision and Pattern Recognition 270
  • Automotive Engineering 62
  • Media Technology 30
  • Artificial Intelligence 107
Replace Shaoyu Chen with:
Shaoyu Chen China
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Adnan Ahmed Rafique Pakistan
Yasutomo Kawanishi Japan
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Countries citing papers authored by Anbumani Subramanian

Since Specialization
Citations

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

Fields of papers citing papers by Anbumani Subramanian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 19 scholars most cited alongside Anbumani Subramanian, 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 Anbumani Subramanian Line = papers co-authored together Anbumani Subramanian links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20238
4 202226
5 202210
6 20222
7 202210
8 202212
9 20199
10 2019192
11 20122
12 20112
13 201111
14 201073
15 20071
16 20077
17 200616
18 20062
19 20031
20 20025

About Anbumani Subramanian

Anbumani Subramanian is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Media Technology, Automotive Engineering and Aerospace Engineering, having authored 21 papers that have together received 390 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (7 papers), Robotics and Sensor-Based Localization (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Autonomous Vehicle Technology and Safety (3 papers), Advanced Vision and Imaging (3 papers), Image Enhancement Techniques (3 papers), Multimodal Machine Learning Applications (3 papers) and Image Processing Techniques and Applications (3 papers). The work is most often cited by research in Human-Computer Interaction (81 citations), Computer Vision and Pattern Recognition (270 citations), Automotive Engineering (62 citations), Media Technology (30 citations) and Artificial Intelligence (107 citations). Anbumani Subramanian has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include C. V. Jawahar, Manmohan Chandraker, Anoop Namboodiri, Girish Varma, Vineeth N Balasubramanian, Chetan Arora, Xiaojin Gong, Daniel J. Stilwell, Kar-Han Tan and Dan Gelb. Their work appears in journals such as Machine Vision and Applications, Journal of the Indian Institute of Science, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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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