Tom Wehrbein

536 total citations · 2 hit papers
4 papers, 303 citations indexed

About

Tom Wehrbein is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Molecular Biology. According to data from OpenAlex, Tom Wehrbein has authored 4 papers receiving a total of 303 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 2 papers in Industrial and Manufacturing Engineering and 1 paper in Molecular Biology. Recurrent topics in Tom Wehrbein's work include Industrial Vision Systems and Defect Detection (2 papers), Microbial Community Ecology and Physiology (1 paper) and Bacteriophages and microbial interactions (1 paper). Tom Wehrbein is often cited by papers focused on Industrial Vision Systems and Defect Detection (2 papers), Microbial Community Ecology and Physiology (1 paper) and Bacteriophages and microbial interactions (1 paper). Tom Wehrbein collaborates with scholars based in Germany, Sweden and Canada. Tom Wehrbein's co-authors include Bodo Rosenhahn, Bastian Wandt, Marco Rudolph, Burkhard Tümmler, Lutz Wiehlmann, Colin Davenport and Patrick Chhatwal and has published in prestigious journals such as BMC Genomics, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

In The Last Decade

Tom Wehrbein

4 papers receiving 302 citations

Hit Papers

align trajectories

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.

Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection

2022 192 citations Marco Rudolph, Tom Wehrbein et al. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) profile →
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

2023 103 citations Marco Rudolph, Tom Wehrbein et al. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tom Wehrbein Germany	3	244	92	84	79	46	4	303
Bastian Wandt Sweden	3	249 1.0×	93 1.0×	85 1.0×	79 1.0×	48 1.0×	4	303
Marco Rudolph Germany	5	255 1.0×	94 1.0×	87 1.0×	81 1.0×	48 1.0×	8	312
Denis Gudovskiy United States	4	253 1.0×	84 0.9×	81 1.0×	91 1.2×	43 0.9×	7	332
Shun Ishizaka Japan	2	248 1.0×	84 0.9×	77 0.9×	82 1.0×	37 0.8×	2	310
Yunkang Cao China	9	209 0.9×	69 0.8×	59 0.7×	75 0.9×	47 1.0×	26	269
Hanqiu Deng Canada	5	382 1.6×	90 1.0×	109 1.3×	143 1.8×	53 1.2×	6	490
Sindy Löwe Netherlands	3	161 0.7×	65 0.7×	43 0.5×	53 0.7×	16 0.3×	4	206
Neelu Madan Denmark	4	216 0.9×	39 0.4×	97 1.2×	99 1.3×	27 0.6×	6	257
Guoyang Xie China	6	164 0.7×	51 0.6×	34 0.4×	64 0.8×	30 0.7×	10	229

Countries citing papers authored by Tom Wehrbein

Since Specialization

Citations

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

Fields of papers citing papers by Tom Wehrbein

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tom Wehrbein

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Wehrbein. A scholar is included among the top collaborators of Tom Wehrbein 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 Tom Wehrbein. Tom Wehrbein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

4 of 4 papers shown

1.

Wehrbein, Tom, et al.. (2025). Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery. 5852–5862. 1 indexed citations

2.

Rudolph, Marco, Tom Wehrbein, Bodo Rosenhahn, & Bastian Wandt. (2023). Asymmetric Student-Teacher Networks for Industrial Anomaly Detection. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2591–2601. 103 indexed citations breakdown →

3.

Wehrbein, Tom, Bodo Rosenhahn, Lutz Wiehlmann, et al.. (2022). Wochenende — modular and flexible alignment-based shotgun metagenome analysis. BMC Genomics. 23(1). 748–748. 7 indexed citations

4.

Rudolph, Marco, Tom Wehrbein, Bodo Rosenhahn, & Bastian Wandt. (2022). Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1829–1838. 192 indexed citations breakdown →

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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