Peter Lehmann

147 papers receiving 1.7k citations

Peers

Peter Lehmann
Comparison fields: 5 of 90
  • Computer Vision and Pattern Recognition 697
  • Biophysics 145
  • Computational Mechanics 484
  • Mechanical Engineering 789
  • Biomedical Engineering 728
Replace Rong Su with:
Rong Su China
Xiangzhao Wang China
Roland Lawrence United States
Suezou Nakadate Japan
Juan C. Miñano Spain
Peter Loosen Germany
Per Gren Sweden
John C. Stover United States
Santiago Royo Spain
Peter Lehmann relative to Rong Su China Rong Su's profile →
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Citations per year

Countries citing papers authored by Peter Lehmann

Since Specialization
Citations

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

Fields of papers citing papers by Peter Lehmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 165 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1998184
2 199894
3 201678
4 199975
5 201253
6 200142
7 201642
8 202038
9 202037
10 201637
11 200436
12 201831
13 199030
14 200626
15 202125
16 201025
17 201925
18 201325
19 200024
20 202024

About Peter Lehmann

Peter Lehmann is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Mechanical Engineering, Computational Mechanics and Electrical and Electronic Engineering, having authored 165 papers that have together received 1.8k indexed citations. Recurring topics across this work include Optical measurement and interference techniques (94 papers), Advanced Measurement and Metrology Techniques (57 papers), Surface Roughness and Optical Measurements (44 papers), Optical Coherence Tomography Applications (33 papers), Near-Field Optical Microscopy (25 papers), Digital Holography and Microscopy (23 papers), Advanced Fluorescence Microscopy Techniques (17 papers) and Solidification and crystal growth phenomena (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (697 citations), Biophysics (145 citations), Computational Mechanics (484 citations), Mechanical Engineering (789 citations) and Biomedical Engineering (728 citations). Peter Lehmann has collaborated with scholars based in Germany, France and United Kingdom. Frequent co-authors include Weichang Xie, R. Moreau, R. Bolcato, D. Camel, Val‚éry Botton, G. Goch, J.P. Garandet, Angelika Brueckner-Foit, Steven M. Gorelick and Dani Or. Their work appears in journals such as Measurement Science and Technology, Optics Express, Journal of Modern Optics, Optics Letters and Journal of Physics Photonics.

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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