Naim Dahnoun

2.4k total citations
77 papers, 1.4k citations indexed

About

Naim Dahnoun is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Automotive Engineering. According to data from OpenAlex, Naim Dahnoun has authored 77 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 25 papers in Electrical and Electronic Engineering and 17 papers in Automotive Engineering. Recurrent topics in Naim Dahnoun's work include Advanced Vision and Imaging (20 papers), Autonomous Vehicle Technology and Safety (17 papers) and Remote Sensing and LiDAR Applications (11 papers). Naim Dahnoun is often cited by papers focused on Advanced Vision and Imaging (20 papers), Autonomous Vehicle Technology and Safety (17 papers) and Remote Sensing and LiDAR Applications (11 papers). Naim Dahnoun collaborates with scholars based in United Kingdom, United States and Spain. Naim Dahnoun's co-authors include Alin Achim, Xiao Ai, Rui Fan, Yifei Wang, Han Cui, John Rarity, Yifei Wang, Jiacheng Wu, Richard Nock and Zhen Zhang and has published in prestigious journals such as IEEE Transactions on Image Processing, Optics Express and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Naim Dahnoun

75 papers receiving 1.3k citations

Peers

Naim Dahnoun
Comparison fields: 5 of 128
  • Computer Vision and Pattern Recognition 572
  • Automotive Engineering 311
  • Electrical and Electronic Engineering 300
  • Artificial Intelligence 190
  • Civil and Structural Engineering 189
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René Ranftl Switzerland
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Citations per field, relative to Naim Dahnoun
Naim Dahnoun · 1×
Citations per year, relative to Naim Dahnoun
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Countries citing papers authored by Naim Dahnoun

Since Specialization
Citations

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

Fields of papers citing papers by Naim Dahnoun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Naim Dahnoun

This figure shows the co-authorship network connecting the top 25 collaborators of Naim Dahnoun. A scholar is included among the top collaborators of Naim Dahnoun 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 Naim Dahnoun. Naim Dahnoun 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 6
2 12
3 16
4 3
5 4
6 36
7 63
8 13
9 11
10 7
11 12
12 48
13 1
14 25
15 3
16 2
17
IEEE Intelligent Vehicles Symposium 2007, Istanbul
7
18
Digital Signal Processing Implementation Using the TMS320C6000 DSP Platform
18
19 2
20 9

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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