Marc Proesmans

2.5k citations
60 papers · 1.2k indexed · 1 hit paper · h-index 14

Marc Proesmans

56 papers receiving 1.1k citations

Hit Papers

Multi-Task Learning for Dense Prediction Tasks: A Survey20212026202220242021100200300400500

Peers

Marc Proesmans
Comparison fields: 5 of 125
  • Computer Vision and Pattern Recognition 756
  • Artificial Intelligence 269
  • Aerospace Engineering 218
  • Geology 114
  • Media Technology 102
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Citations per year

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
#WorkIndexed citations
1 29
2
Learning To Classify Images Without Labels.
7
3 1
4 37
5 3
6 9
7 6
8
Towards a combined use of IR, UV and 3D-imaging for the study of small decorated and inscribed artefacts
1
9 8
10
On the surface and beyond. An new approach with Multispectral Photometric Stereo to assess Illuminated Manuscripts and their condition
2
11 1
12 1
13 10
14
Special Lecture: 3D Modeling for Communications
1
15 24
16 1
17 46
18 29
19 10
20
Parallel segmentation algorithms
1

About Marc Proesmans

Marc Proesmans is a scholar working on Space and Planetary Science, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 60 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (14 papers), 3D Surveying and Cultural Heritage (10 papers) and Image Processing and 3D Reconstruction (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (756 citations), Geology (114 citations) and Computer Graphics and Computer-Aided Design (68 citations). Marc Proesmans has collaborated with scholars based in Belgium, Switzerland and Italy. Frequent 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. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Physics A and Image and Vision Computing.

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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