Markus Eisenbach

972 citations
27 papers · 646 · 1 hit paper · h-index 12

Impact in

Papers in

Markus Eisenbach

25 papers receiving 628 citations

Markus Eisenbach's Hit Papers

How to get pavement distress detection ready for deep learning? A systematic approach 2017 · 291 citations
2910+3+6Years since publication50100150200250

Peers

Markus Eisenbach
Comparison fields: 5 of 63
  • Civil and Structural Engineering 356
  • Computer Vision and Pattern Recognition 200
  • Human-Computer Interaction 34
  • Industrial and Manufacturing Engineering 60
  • Geology 24
Replace Klaus Debes with:
Klaus Debes Germany
Ronny Stricker Germany
Lasitha Piyathilaka Australia
Zhaoyun Sun China
Hiromitsu Fujii Japan
Daniel Seichter Germany
Ziji Ma China
Gang Pan China
Zhaozheng Hu China
Quoc‐Viet Tran Taiwan
Markus Eisenbach relative to Klaus Debes Germany Klaus Debes's profile →
Citations per field
00.5×1.7×
Klaus Debes · 1×
Citations per year

Countries citing papers authored by Markus Eisenbach

Since Specialization
Citations

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

Fields of papers citing papers by Markus Eisenbach

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Markus Eisenbach, 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 Markus Eisenbach Line = papers co-authored together Markus Eisenbach links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1
How to get pavement distress detection ready for deep learning? A systematic approach
Hit paper breakdown →
2017291
2 202365
3 201957
4 201642
5 201522
6 201719
7 201519
8 201215
9 201214
10 202013
11 201612
12 201812
13
May I be your Personal Coach? Bringing Together Person Tracking and Visual Re-identification on a Mobile Robot
20169
14 20238
15 20128
16 20138
17 20235
18 20235
19 20244
20 20194

About Markus Eisenbach

Markus Eisenbach is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Civil and Structural Engineering, Control and Systems Engineering and Artificial Intelligence, having authored 27 papers that have together received 646 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (12 papers), Human Pose and Action Recognition (11 papers), Infrastructure Maintenance and Monitoring (4 papers), Advanced Neural Network Applications (4 papers), Advanced Vision and Imaging (4 papers), Hand Gesture Recognition Systems (3 papers), Remote Sensing and LiDAR Applications (3 papers) and Asphalt Pavement Performance Evaluation (3 papers). The work is most often cited by research in Civil and Structural Engineering (356 citations), Computer Vision and Pattern Recognition (200 citations), Human-Computer Interaction (34 citations), Industrial and Manufacturing Engineering (60 citations) and Geology (24 citations). Markus Eisenbach has collaborated with scholars based in Germany. Frequent co-authors include Horst–Michael Groß, Klaus Debes, Ronny Stricker, Daniel Seichter, Karl Amende, Christian Martín, E. Einhorn, A. Bley, Andrea Scheidig and Steffen Mueller. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Sensors, Autonomous Robots, Electronics and Common Library Network (Der Gemeinsame Bibliotheksverbund).

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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