Markus Eisenbach

946 total citations · 1 hit paper
27 papers, 615 citations indexed

About

Markus Eisenbach is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Civil and Structural Engineering. According to data from OpenAlex, Markus Eisenbach has authored 27 papers receiving a total of 615 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 6 papers in Human-Computer Interaction and 4 papers in Civil and Structural Engineering. Recurrent topics in Markus Eisenbach's work include Video Surveillance and Tracking Methods (12 papers), Human Pose and Action Recognition (11 papers) and Advanced Neural Network Applications (4 papers). Markus Eisenbach is often cited by papers focused on Video Surveillance and Tracking Methods (12 papers), Human Pose and Action Recognition (11 papers) and Advanced Neural Network Applications (4 papers). Markus Eisenbach collaborates with scholars based in Germany. Markus Eisenbach's co-authors include Horst–Michael Groß, Klaus Debes, Ronny Stricker, Daniel Seichter, Karl Amende, Steffen Mueller, E. Einhorn, Christian Martín, A. Bley and Andrea Scheidig and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Markus Eisenbach

25 papers receiving 595 citations

Hit Papers

How to get pavement distress detection ready for deep lea... 2017 2026 2020 2023 2017 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. How to get pavement distress detection ready for deep learning? A systematic approach
    2017 281 citations Markus Eisenbach, Ronny Stricker et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Markus Eisenbach Germany 12 344 195 96 64 57 27 615
Klaus Debes Germany 11 347 1.0× 132 0.7× 104 1.1× 62 1.0× 41 0.7× 20 618
Ronny Stricker Germany 7 358 1.0× 63 0.3× 114 1.2× 63 1.0× 42 0.7× 12 707
Zhaoyun Sun China 13 324 0.9× 85 0.4× 88 0.9× 65 1.0× 37 0.6× 46 531
Zhaozheng Hu China 14 194 0.6× 273 1.4× 66 0.7× 63 1.0× 34 0.6× 63 687
Khashayar Asadi United States 9 156 0.5× 124 0.6× 61 0.6× 16 0.3× 52 0.9× 12 561
Xiangrong Xu China 11 125 0.4× 239 1.2× 132 1.4× 122 1.9× 126 2.2× 71 652
Hiromitsu Fujii Japan 12 97 0.3× 139 0.7× 72 0.8× 68 1.1× 32 0.6× 74 353
Peggy Subirats France 7 272 0.8× 183 0.9× 61 0.6× 46 0.7× 66 1.2× 22 516
Lasitha Piyathilaka Australia 10 83 0.2× 126 0.6× 30 0.3× 34 0.5× 17 0.3× 34 374
Ziji Ma China 15 215 0.6× 189 1.0× 246 2.6× 10 0.2× 148 2.6× 51 601

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-authorship network of co-authors of Markus Eisenbach

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Eisenbach. A scholar is included among the top collaborators of Markus Eisenbach 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 Markus Eisenbach. Markus Eisenbach 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
1.
Eisenbach, Markus, et al.. (2024). Detection of Novel Objects without Fine-Tuning in Assembly Scenarios by Class-Agnostic Object Detection and Object Re-Identification. SHILAP Revista de lepidopterología. 5(3). 373–406.
2.
Eisenbach, Markus, et al.. (2024). Unsupervised Anomaly Detection and Explanation in Network Traffic with Transformers. Electronics. 13(22). 4570–4570. 2 indexed citations
3.
Eisenbach, Markus, et al.. (2023). Few-Shot Object Detection: A Comprehensive Survey. IEEE Transactions on Neural Networks and Learning Systems. 35(9). 11958–11978. 54 indexed citations
4.
Eisenbach, Markus, et al.. (2023). ATTACH Dataset: Annotated Two-Handed Assembly Actions for Human Action Understanding. 11367–11373. 6 indexed citations
5.
Eisenbach, Markus, et al.. (2023). How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks. 1–9. 5 indexed citations
6.
Eisenbach, Markus, et al.. (2023). A Little Bit Attention Is All You Need for Person Re-Identification. 7598–7605. 5 indexed citations
7.
Eisenbach, Markus, et al.. (2022). On the Importance of Label Encoding and Uncertainty Estimation for Robotic Grasp Detection. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4781–4788. 2 indexed citations
8.
Stricker, Ronny, et al.. (2019). Deep learning for automatic detection and classification of road damage from mobile LiDAR data. Common Library Network (Der Gemeinsame Bibliotheksverbund). 1 indexed citations
9.
Fuchs, Stefan, et al.. (2019). Real-time 3D Pose Estimation from Single Depth Images. 716–724. 4 indexed citations
10.
11.
Seichter, Daniel, Markus Eisenbach, Ronny Stricker, & Horst–Michael Groß. (2018). How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort. 63–70. 11 indexed citations
12.
Eisenbach, Markus, Ronny Stricker, Daniel Seichter, et al.. (2017). How to get pavement distress detection ready for deep learning? A systematic approach. 2039–2047. 281 indexed citations breakdown →
13.
Eisenbach, Markus, et al.. (2016). May I be your Personal Coach? Bringing Together Person Tracking and Visual Re-identification on a Mobile Robot. International Symposium on Robotics. 1–8. 9 indexed citations
14.
Groß, Horst–Michael, Andrea Scheidig, Klaus Debes, et al.. (2016). ROREAS: robot coach for walking and orientation training in clinical post-stroke rehabilitation—prototype implementation and evaluation in field trials. Autonomous Robots. 41(3). 679–698. 42 indexed citations
15.
Eisenbach, Markus, et al.. (2016). Cooperative multi-scale Convolutional Neural Networks for person detection. 267–276. 12 indexed citations
16.
Eisenbach, Markus, et al.. (2015). Evaluation of multi feature fusion at score-level for appearance-based person re-identification. 1–8. 22 indexed citations
17.
Eisenbach, Markus, et al.. (2012). Automatic calibration of a stationary network of laser range finders by matching movement trajectories. 431–437. 13 indexed citations
18.
19.
Eisenbach, Markus, et al.. (2012). Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories. 3. 306–312. 3 indexed citations
20.
Eisenbach, Markus, et al.. (2012). Vision-based hyper-real-time object tracker for robotic applications. 2108–2115. 8 indexed citations

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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