Christopher Diehl

2.1k citations
24 papers · 1.4k indexed · 1 hit paper · h-index 11

Impact in

Papers in

Christopher Diehl

18 papers receiving 1.3k citations

Hit Papers

Link mining 2005 · 607 citations
6072005202620122019200400600

Peers

Christopher Diehl
Comparison fields: 5 of 115
  • Statistical and Nonlinear Physics 430
  • Media Technology 264
  • Artificial Intelligence 745
  • Computational Mathematics 10
  • Computer Vision and Pattern Recognition 291
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Citations per field
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Citations per year

Countries citing papers authored by Christopher Diehl

Since Specialization
Citations

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

Fields of papers citing papers by Christopher Diehl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 20250
2 202319
3 20223
4 20220
5 20223
6 20180
7 20091
8
Relationship identification for social network discovery
200765
9 200625
10 200528
11 20057
12 200458
13 20044
14 2004180
15 200336
16 200232
17 20027
18 20010
19 20005
20 199923

About Christopher Diehl

Christopher Diehl is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Artificial Intelligence, Statistical and Nonlinear Physics and Aerospace Engineering, having authored 24 papers that have together received 1.4k indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (6 papers), Robotics and Sensor-Based Localization (5 papers), Robotic Path Planning Algorithms (5 papers), Autonomous Vehicle Technology and Safety (4 papers), Complex Network Analysis Techniques (3 papers), Advanced Vision and Imaging (3 papers), Anomaly Detection Techniques and Applications (3 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (430 citations), Media Technology (264 citations), Artificial Intelligence (745 citations), Computational Mathematics (10 citations) and Computer Vision and Pattern Recognition (291 citations). Christopher Diehl has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Lise Getoor, Amit Banerjee, Philippe Burlina, Gert Cauwenberghs, Galileo Namata, J.B. Hampshire, Cash Costello, Pradeep K. Khosla, P.K. Khosla and Torsten Bertram. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Robotics and Automation, IEEE Transactions on Geoscience and Remote Sensing, IFAC-PapersOnLine and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

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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