Marc Proesmans

2.5k total citations · 1 hit paper
60 papers, 1.2k citations indexed

About

Marc Proesmans is a scholar working on Computer Vision and Pattern Recognition, Geology and Artificial Intelligence. According to data from OpenAlex, Marc Proesmans has authored 60 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Computer Vision and Pattern Recognition, 10 papers in Geology and 8 papers in Artificial Intelligence. Recurrent topics in Marc Proesmans's work include Advanced Vision and Imaging (14 papers), 3D Surveying and Cultural Heritage (10 papers) and Image Processing and 3D Reconstruction (8 papers). Marc Proesmans is often cited by papers focused on Advanced Vision and Imaging (14 papers), 3D Surveying and Cultural Heritage (10 papers) and Image Processing and 3D Reconstruction (8 papers). Marc Proesmans collaborates with scholars based in Belgium, Switzerland and Italy. Marc Proesmans's co-authors include Luc Van Gool, Stamatios Georgoulis, Simon Vandenhende, Dengxin Dai, Wouter Van Gansbeke, Theo Moons, Tinne Tuytelaars, A. Oosterlinck, Yevhen Kuznietsov and Luc J. Van Gool and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Physics A and Image and Vision Computing.

In The Last Decade

Marc Proesmans

56 papers receiving 1.1k citations

Hit Papers

Multi-Task Learning for Dense Prediction Tasks: A Survey 2021 2026 2022 2024 2021 100 200 300 400 500
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. Multi-Task Learning for Dense Prediction Tasks: A Survey
    2021 525 citations Simon Vandenhende, Stamatios Georgoulis et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marc Proesmans Belgium 14 756 269 218 114 102 60 1.2k
Tianfu Wu United States 21 982 1.3× 218 0.8× 300 1.4× 171 1.5× 111 1.1× 79 1.5k
Yukang Chen China 22 985 1.3× 280 1.0× 319 1.5× 132 1.2× 96 0.9× 45 1.5k
João Carreira United States 18 1.6k 2.1× 349 1.3× 338 1.6× 87 0.8× 133 1.3× 30 1.9k
Aoran Xiao Singapore 18 649 0.9× 484 1.8× 136 0.6× 118 1.0× 91 0.9× 29 1.1k
Xuran Pan China 11 989 1.3× 331 1.2× 256 1.2× 118 1.0× 313 3.1× 16 1.7k
Qi She Hong Kong 15 779 1.0× 458 1.7× 148 0.7× 48 0.4× 118 1.2× 33 1.3k
Christian Schütz Switzerland 8 644 0.9× 219 0.8× 286 1.3× 88 0.8× 78 0.8× 23 1.1k
Zsolt Kira United States 22 1.2k 1.6× 765 2.8× 245 1.1× 151 1.3× 54 0.5× 68 1.9k
Xinyu Huang United States 13 641 0.8× 131 0.5× 184 0.8× 46 0.4× 119 1.2× 36 991
Dayan Guan Singapore 18 859 1.1× 518 1.9× 170 0.8× 97 0.9× 221 2.2× 26 1.3k

Countries citing papers authored by Marc Proesmans

Since Specialization
Citations

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

Fields of papers citing papers by Marc Proesmans

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marc Proesmans

This figure shows the co-authorship network connecting the top 25 collaborators of Marc Proesmans. A scholar is included among the top collaborators of Marc Proesmans 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 Marc Proesmans. Marc Proesmans 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.
Kuznietsov, Yevhen, Marc Proesmans, & Luc Van Gool. (2021). CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences. Lirias (KU Leuven). 2906–2916. 29 indexed citations
2.
Gansbeke, Wouter Van, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, & Luc Van Gool. (2020). Learning To Classify Images Without Labels.. arXiv (Cornell University). 7 indexed citations
3.
Neven, Davy, et al.. (2020). Object-Centric Street Scene Synthesis with Generative Adversarial Networks. Lirias (KU Leuven). 665–671. 1 indexed citations
4.
Vandenhende, Simon, et al.. (2020). Multi-Task Learning for Dense Prediction Tasks: A Survey. Lirias (KU Leuven). 37 indexed citations
5.
Proesmans, Marc, et al.. (2019). Uncertainty based model selection for fast semantic segmentation. 1–6. 3 indexed citations
6.
Gool, Luc Van, et al.. (2018). Bridging Multi-light & Multi-Spectral images to study, preserve and disseminate archival documents. Archiving Conference. 15(1). 64–69. 9 indexed citations
7.
Georgoulis, Stamatios, et al.. (2017). Material Classification under Natural Illumination Using Reflectance Maps. Lirias (KU Leuven). 244–253. 6 indexed citations
8.
Proesmans, Marc, et al.. (2016). Towards a combined use of IR, UV and 3D-imaging for the study of small decorated and inscribed artefacts. Ghent University Academic Bibliography (Ghent University). 1 indexed citations
9.
Georgoulis, Stamatios, et al.. (2015). A Gaussian Process Latent Variable Model for BRDF Inference. Lirias (KU Leuven). 3559–3567. 8 indexed citations
10.
Proesmans, Marc, et al.. (2015). On the surface and beyond. An new approach with Multispectral Photometric Stereo to assess Illuminated Manuscripts and their condition. Lirias (KU Leuven). 1. 103–103. 2 indexed citations
11.
Janssens, Koen, Geert Van der Snickt, Matthias Alfeld, et al.. (2014). Young Anthony van Dyck Revisited: A Multidisciplinary Approach to a Portrait Once Attributed to Peter Paul Rubens. Lirias (KU Leuven). 6. 21–35. 1 indexed citations
12.
Proesmans, Marc, Els Pauwels, Luc J. Van Gool, Theo Moons, & A. Oosterlinck. (2002). Image enhancement using non-linear diffusion. 680–681. 1 indexed citations
13.
Gool, Luc Van, et al.. (2002). Affine reconstruction from perspective image pairs obtained by a translating camera. 1. 290–294. 10 indexed citations
14.
Gool, Luc Van, et al.. (1998). Special Lecture: 3D Modeling for Communications. 482–489. 1 indexed citations
15.
Gool, Luc Van, Marc Proesmans, & Andrew Zisserman. (1998). Planar homologies as a basis for grouping and recognition. Image and Vision Computing. 16(1). 21–26. 24 indexed citations
16.
Proesmans, Marc, et al.. (1997). <title>Recognition of suspects through 3D photographs</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2942. 170–181. 1 indexed citations
17.
Moons, Theo, Luc Van Gool, Marc Proesmans, & Els Pauwels. (1996). Affine reconstruction from perspective image pairs with a relative object-camera translation in between. IEEE Transactions on Pattern Analysis and Machine Intelligence. 18(1). 77–83. 46 indexed citations
18.
Gool, Luc Van, Theo Moons, & Marc Proesmans. (1996). Mirror and point symmetry under perspective skewing. 285–292. 29 indexed citations
19.
Gool, Luc Van, et al.. (1995). Grouping and invariants using planar homologies. IDCases. 24. 182–189. 10 indexed citations
20.
Proesmans, Marc & A. Oosterlinck. (1993). Parallel segmentation algorithms. John Wiley & Sons, Inc. eBooks. 123–146. 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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