Mathis Hoffmann

481 total citations
11 papers, 321 citations indexed

About

Mathis Hoffmann is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Renewable Energy, Sustainability and the Environment. According to data from OpenAlex, Mathis Hoffmann has authored 11 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Electrical and Electronic Engineering and 4 papers in Renewable Energy, Sustainability and the Environment. Recurrent topics in Mathis Hoffmann's work include Integrated Circuits and Semiconductor Failure Analysis (4 papers), Photovoltaic System Optimization Techniques (4 papers) and Industrial Vision Systems and Defect Detection (3 papers). Mathis Hoffmann is often cited by papers focused on Integrated Circuits and Semiconductor Failure Analysis (4 papers), Photovoltaic System Optimization Techniques (4 papers) and Industrial Vision Systems and Defect Detection (3 papers). Mathis Hoffmann collaborates with scholars based in Germany and United States. Mathis Hoffmann's co-authors include Andreas Maier, Vincent Christlein, Tobias Würfl, Katharina Breininger, Mathias Unberath, Yixing Huang, Ian Marius Peters, Claudia Buerhop‐Lutz, Christoph J. Brabec and Jens Hauch and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Medical Imaging and Solar Energy Materials and Solar Cells.

In The Last Decade

Mathis Hoffmann

9 papers receiving 310 citations

Peers

Mathis Hoffmann

RNMI

RESE

EEE

CVPR

Hong Shangguan China

Pengcheng Zhang China

Sun Zheng China

Sahadev Poudel South Korea

Changcheng Gong China

Adel Oulefki Algeria

Arslan Dawood Butt Pakistan

Zhiyang Yao China

Siqi Liang China

Thaweesak Trongtirakul United States

Hong Shangguan China

Mathis Hoffmann

167 ×1.2

RNMI

131 ×1.1

41 ×0.4

RESE

32 ×0.5

EEE

141 ×2.2

CVPR

Citations per year, relative to Mathis Hoffmann Mathis Hoffmann (= 1×) peers Hong Shangguan

Countries citing papers authored by Mathis Hoffmann

Since Specialization

Citations

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

Fields of papers citing papers by Mathis Hoffmann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mathis Hoffmann

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

All Works

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11 of 11 papers shown

Bernhardt, Philipp, et al.. (2025). Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography. Scientific Reports. 15(1). 15186–15186.

Lüer, Larry, Karen Forberich, Johannes Hepp, et al.. (2023). PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network. Solar Energy Materials and Solar Cells. 264. 112621–112621. 5 indexed citations

Hoffmann, Mathis, Claudia Buerhop‐Lutz, Tobias Pickel, et al.. (2022). Anomaly detection in IR images of PV modules using supervised contrastive learning. Progress in Photovoltaics Research and Applications. 30(6). 597–614. 37 indexed citations

Hoffmann, Mathis, Johannes Hepp, Claudia Buerhop‐Lutz, et al.. (2021). Module-Power Prediction from PL Measurements using Deep Learning. 185. 827–830. 2 indexed citations

Hoffmann, Mathis, Thomas Köhler, Ian Marius Peters, et al.. (2021). Joint Superresolution and Rectification for Solar Cell Inspection. IEEE Journal of Photovoltaics. 11(4). 1051–1058.

Hoffmann, Mathis, Claudia Buerhop‐Lutz, Tobias Pickel, et al.. (2021). Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements. Progress in Photovoltaics Research and Applications. 29(8). 920–935. 20 indexed citations

Hepp, Johannes, Mathis Hoffmann, Claudia Buerhop‐Lutz, et al.. (2021). Photoluminescence for Defect Detection on Full-Sized Photovoltaic Modules. IEEE Journal of Photovoltaics. 11(6). 1419–1429. 44 indexed citations

Hoffmann, Mathis, et al.. (2019). Weakly Supervised Segmentation of Cracks on Solar Cells Using Normalized L_p Norm. 1885–1889. 44 indexed citations

Würfl, Tobias, et al.. (2019). Calibration‐free beam hardening reduction in x‐ray CBCT using the epipolar consistency condition and physical constraints. Medical Physics. 46(12). e810–e822. 2 indexed citations

10.

Würfl, Tobias, Mathis Hoffmann, Vincent Christlein, et al.. (2018). Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems. IEEE Transactions on Medical Imaging. 37(6). 1454–1463. 166 indexed citations

11.

Hoffmann, Mathis, et al.. (2017). A Robust Chessboard Detector for Geometric Camera Calibration. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 34–43. 1 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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