Roberto Rigamonti

794 total citations
10 papers, 548 citations indexed

About

Roberto Rigamonti is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Roberto Rigamonti has authored 10 papers receiving a total of 548 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Computational Mechanics and 4 papers in Artificial Intelligence. Recurrent topics in Roberto Rigamonti's work include Sparse and Compressive Sensing Techniques (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Retinal Imaging and Analysis (3 papers). Roberto Rigamonti is often cited by papers focused on Sparse and Compressive Sensing Techniques (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Retinal Imaging and Analysis (3 papers). Roberto Rigamonti collaborates with scholars based in Switzerland, Italy and Austria. Roberto Rigamonti's co-authors include Vincent Lepetit, Pascal Fua, Amos Sironi, Matthew A. Brown, Carlos Becker, Bugra Tekin, Domenico G. Sorrenti, Matteo Matteucci, Davide Migliore and Germán González and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Computer Vision and Image Understanding and Lecture notes in computer science.

In The Last Decade

Roberto Rigamonti

9 papers receiving 526 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roberto Rigamonti Switzerland 8 413 123 102 96 54 10 548
Amos Sironi Switzerland 7 282 0.7× 89 0.7× 73 0.7× 43 0.4× 45 0.8× 11 429
S.B. Gokturk United States 11 601 1.5× 122 1.0× 150 1.5× 26 0.3× 107 2.0× 18 874
Daniel Kondermann Germany 11 370 0.9× 61 0.5× 128 1.3× 23 0.2× 58 1.1× 24 574
K. Satya Prasad India 15 328 0.8× 145 1.2× 64 0.6× 29 0.3× 103 1.9× 91 625
Isaac Cohen United States 14 531 1.3× 101 0.8× 66 0.6× 72 0.8× 18 0.3× 33 684
Hanli Zhao China 16 355 0.9× 190 1.5× 50 0.5× 59 0.6× 79 1.5× 54 646
Ning Xu China 13 529 1.3× 120 1.0× 89 0.9× 85 0.9× 150 2.8× 53 816
D. Reisfeld Israel 10 612 1.5× 69 0.6× 36 0.4× 90 0.9× 75 1.4× 20 760
Thomas P. Weldon United States 12 294 0.7× 64 0.5× 47 0.5× 26 0.3× 117 2.2× 63 603
Xavier Binefa Spain 16 771 1.9× 97 0.8× 85 0.8× 43 0.4× 45 0.8× 59 981

Countries citing papers authored by Roberto Rigamonti

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Rigamonti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roberto Rigamonti

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

All Works

10 of 10 papers shown
1.
Sironi, Amos, Bugra Tekin, Roberto Rigamonti, Vincent Lepetit, & Pascal Fua. (2014). Learning Separable Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(1). 94–106. 66 indexed citations
2.
Rigamonti, Roberto, Vincent Lepetit, Germán González, et al.. (2014). On the relevance of sparsity for image classification. Computer Vision and Image Understanding. 125. 115–127. 7 indexed citations
3.
Rigamonti, Roberto, Amos Sironi, Vincent Lepetit, & Pascal Fua. (2013). Learning Separable Filters. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2754–2761. 138 indexed citations
4.
Becker, Carlos, Roberto Rigamonti, Vincent Lepetit, & Pascal Fua. (2013). Supervised Feature Learning for Curvilinear Structure Segmentation. Lecture notes in computer science. 16(Pt 1). 526–533. 116 indexed citations
5.
Becker, Carlos, Roberto Rigamonti, Vincent Lepetit, & Pascal Fua. (2013). KernelBoost: Supervised Learning of Image Features For Classification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4 indexed citations
6.
Rigamonti, Roberto & Vincent Lepetit. (2012). Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters. Lecture notes in computer science. 15(Pt 1). 189–197. 30 indexed citations
7.
Rigamonti, Roberto, Engin Türetken, Germán González, Pascal Fua, & Vincent Lepetit. (2011). Filter Learning for Linear Structure Segmentation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 7 indexed citations
8.
Rigamonti, Roberto, Matthew A. Brown, & Vincent Lepetit. (2011). Are sparse representations really relevant for image classification?. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1545–1552. 140 indexed citations
9.
Rigamonti, Roberto, Matthew Brown, & Vincent Lepetit. (2010). Is Sparsity Really Relevant for Image Classification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
10.
Migliore, Davide, et al.. (2009). Use a Single Camera for Simultaneous Localization And Mapping with Mobile Object Tracking in dynamic environments. BOA (University of Milano-Bicocca). 4–9. 39 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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