Jan Lellmann

1.3k total citations
24 papers, 469 citations indexed

About

Jan Lellmann is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Jan Lellmann has authored 24 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 8 papers in Computational Mechanics and 6 papers in Artificial Intelligence. Recurrent topics in Jan Lellmann's work include Sparse and Compressive Sensing Techniques (8 papers), Medical Image Segmentation Techniques (8 papers) and Image and Signal Denoising Methods (3 papers). Jan Lellmann is often cited by papers focused on Sparse and Compressive Sensing Techniques (8 papers), Medical Image Segmentation Techniques (8 papers) and Image and Signal Denoising Methods (3 papers). Jan Lellmann collaborates with scholars based in Germany, United Kingdom and Sweden. Jan Lellmann's co-authors include Christoph Schnörr, Carola‐Bibiane Schönlieb, Florian Becker, Dirk A. Lorenz, Tuomo Valkonen, Frank Lenzen, Daniel Cremers, Evgeny Strekalovskiy, Sebastian Nowozin and Bjoern Andres and has published in prestigious journals such as Proceedings of the National Academy of Sciences, International Journal of Molecular Sciences and International Journal of Computer Vision.

In The Last Decade

Jan Lellmann

20 papers receiving 448 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan Lellmann Germany 11 222 107 74 49 44 24 469
Xiaohao Cai United Kingdom 14 295 1.3× 120 1.1× 129 1.7× 10 0.2× 79 1.8× 42 722
Mauro Piccioni Italy 13 203 0.9× 59 0.6× 180 2.4× 68 1.4× 38 0.9× 41 583
Stephan Huckemann Germany 14 180 0.8× 43 0.4× 108 1.5× 62 1.3× 25 0.6× 41 589
Qing Qu United States 13 159 0.7× 257 2.4× 98 1.3× 48 1.0× 10 0.2× 43 649
Andreas Weinmann Germany 12 209 0.9× 170 1.6× 24 0.3× 29 0.6× 53 1.2× 55 513
Julien Rabin France 11 293 1.3× 54 0.5× 81 1.1× 25 0.5× 17 0.4× 23 508
Hong‐Ye Gao United States 8 643 2.9× 154 1.4× 54 0.7× 17 0.3× 30 0.7× 13 1.0k
Xiliang Lu China 16 265 1.2× 332 3.1× 89 1.2× 52 1.1× 27 0.6× 67 830
Pierluigi Maponi Italy 13 39 0.2× 46 0.4× 32 0.4× 27 0.6× 26 0.6× 70 417
Mansoor Rezghi Iran 13 158 0.7× 59 0.6× 85 1.1× 32 0.7× 16 0.4× 33 427

Countries citing papers authored by Jan Lellmann

Since Specialization
Citations

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

Fields of papers citing papers by Jan Lellmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jan Lellmann

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Lellmann. A scholar is included among the top collaborators of Jan Lellmann 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 Jan Lellmann. Jan Lellmann 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.
Hempel, Benjamin-Florian, Herbert Thiele, Jan Lellmann, et al.. (2025). Integrating MALDI-MSI-Based Spatial Proteomics and Machine Learning to Predict Chemoradiotherapy Outcomes in Head and Neck Cancer. International Journal of Molecular Sciences. 26(18). 9084–9084.
2.
Lellmann, Jan, Herbert Thiele, Steve E. Kalloger, et al.. (2023). Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers. 15(3). 686–686. 4 indexed citations
3.
Lellmann, Jan, et al.. (2023). A universal quantum algorithm for weighted maximum cut and Ising problems. Quantum Information Processing. 22(7). 1 indexed citations
4.
5.
Lellmann, Jan, et al.. (2022). An Iterative Quantum Approach for Transformation Estimation from Point Sets. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 519–527. 5 indexed citations
6.
Heldmann, Stefan, et al.. (2022). Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition. Journal of Mathematical Imaging and Vision. 64(2). 194–211. 3 indexed citations
7.
Lellmann, Jan, et al.. (2020). Higher-Order Total Directional Variation. Part I: Imaging Applications. HAL (Le Centre pour la Communication Scientifique Directe). 19 indexed citations
8.
Lellmann, Jan, et al.. (2019). Methods for Finding the Offset in Robust Subspace Fitting. PAMM. 19(1).
9.
Klein, Oliver, Hagen Kulbe, Paul Jank, et al.. (2018). MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods. PROTEOMICS - CLINICAL APPLICATIONS. 13(1). e1700181–e1700181. 54 indexed citations
10.
Lee, Juheon, Xiaohao Cai, Jan Lellmann, et al.. (2016). Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(6). 2554–2567. 53 indexed citations
11.
Kappes, Jörg Hendrik, Bjoern Andres, Fred A. Hamprecht, et al.. (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115(2). 155–184. 77 indexed citations
12.
Lenzen, Frank, Jan Lellmann, Florian Becker, & Christoph Schnörr. (2014). Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets. SIAM Journal on Imaging Sciences. 7(4). 2139–2174. 5 indexed citations
13.
Lellmann, Jan, Dirk A. Lorenz, Carola‐Bibiane Schönlieb, & Tuomo Valkonen. (2014). Imaging with Kantorovich--Rubinstein Discrepancy. SIAM Journal on Imaging Sciences. 7(4). 2833–2859. 53 indexed citations
14.
Lellmann, Jan, et al.. (2013). Total Variation Regularization for Functions with Values in a Manifold. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 2944–2951. 29 indexed citations
15.
Lenzen, Frank, Florian Becker, Jan Lellmann, Stefania Petra, & Christoph Schnörr. (2012). A class of quasi-variational inequalities for adaptive image denoising and decomposition. Computational Optimization and Applications. 54(2). 371–398. 26 indexed citations
16.
Lellmann, Jan, Frank Lenzen, & Christoph Schnörr. (2012). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. 47(3). 239–257. 7 indexed citations
17.
Lellmann, Jan. (2011). Nonsmooth Convex Variational Approaches to Image Analysis. heiDOK (Heidelberg University). 1 indexed citations
18.
Lellmann, Jan & Christoph Schnörr. (2011). Continuous Multiclass Labeling Approaches and Algorithms. SIAM Journal on Imaging Sciences. 4(4). 1049–1096. 66 indexed citations
19.
Lellmann, Jan, et al.. (2011). SPARSE TEMPLATE-BASED VARIATIONAL IMAGE SEGMENTATION. 3(01n02). 149–166. 1 indexed citations
20.
Lellmann, Jan, Jonathan Balzer, Andreas Rieder, & Jürgen Beyerer. (2008). Shape from Specular Reflection and Optical Flow. International Journal of Computer Vision. 80(2). 226–241. 21 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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