Markus Eisenbach

972 citations
32 papers · 726 · 1 hit paper · h-index 13

Impact in

Papers in

Markus Eisenbach

31 papers receiving 707 citations

Markus Eisenbach's Hit Papers

How to get pavement distress detection ready for deep learning? A systematic approach 2017 · 309 citations
3090+3+6Years since publication100200300

Peers

Markus Eisenbach
Comparison fields: 5 of 65
  • Civil and Structural Engineering 380
  • Computer Vision and Pattern Recognition 239
  • Human-Computer Interaction 45
  • Industrial and Manufacturing Engineering 68
  • Ocean Engineering 69
Replace Klaus Debes with:
Klaus Debes Germany
Ronny Stricker Germany
Kisung You United States
Lasitha Piyathilaka Australia
Tao Jin China
Haosen Chen China
Ziji Ma China
Zhaoyun Sun China
Zhaozheng Hu China
Gang Pan China
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 14 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 32 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 →
2017309
2 202370
3 201964
4 201650
5 201523
6 201722
7 201521
8 201215
9 201614
10 201214
11 201114
12 202013
13 201813
14 202311
15
May I be your Personal Coach? Bringing Together Person Tracking and Visual Re-identification on a Mobile Robot
20169
16 20139
17 20128
18 20236
19 20196
20 20236

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 Biomedical Engineering, having authored 32 papers that have together received 726 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (14 papers), Human Pose and Action Recognition (12 papers), Infrastructure Maintenance and Monitoring (5 papers), Advanced Neural Network Applications (4 papers), Hand Gesture Recognition Systems (4 papers), Advanced Vision and Imaging (4 papers), Asphalt Pavement Performance Evaluation (4 papers) and Remote Sensing and LiDAR Applications (3 papers). The work is most often cited by research in Civil and Structural Engineering (380 citations), Computer Vision and Pattern Recognition (239 citations), Human-Computer Interaction (45 citations), Industrial and Manufacturing Engineering (68 citations) and Ocean Engineering (69 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, Andrea Scheidig, A. Bley, Steffen Mueller, Christian Martín and E. Einhorn. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Sensors, Electronics, Autonomous Robots and Lecture notes in computer science.

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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