Leonardo Galteri

1.2k total citations
24 papers, 709 citations indexed

About

Leonardo Galteri is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Leonardo Galteri has authored 24 papers receiving a total of 709 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 2 papers in Signal Processing. Recurrent topics in Leonardo Galteri's work include Advanced Image Processing Techniques (12 papers), Generative Adversarial Networks and Image Synthesis (8 papers) and Image and Signal Denoising Methods (6 papers). Leonardo Galteri is often cited by papers focused on Advanced Image Processing Techniques (12 papers), Generative Adversarial Networks and Image Synthesis (8 papers) and Image and Signal Denoising Methods (6 papers). Leonardo Galteri collaborates with scholars based in Italy. Leonardo Galteri's co-authors include Alberto Del Bimbo, Irene Amerini, Roberto Caldelli, Marco Bertini, Lorenzo Seidenari, Claudio Ferrari, Giuseppe Lisanti, Stefano Berretti, Tiberio Uricchio and Andrew D. Bagdanov and has published in prestigious journals such as IEEE Transactions on Image Processing, Pattern Recognition Letters and IEEE Transactions on Multimedia.

In The Last Decade

Leonardo Galteri

24 papers receiving 693 citations

Peers

Leonardo Galteri
Comparison fields: 5 of 67
  • Computer Vision and Pattern Recognition 639
  • Artificial Intelligence 130
  • Signal Processing 55
  • Media Technology 46
  • Electrical and Electronic Engineering 18
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Sheng-Yu Wang United States
Guangnan Ye United States
Jen-Hao Hsiao United States
Chih‐Yi Chiu Taiwan
Tobias Hinz Germany
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Belhassen Bayar United States
Jiwei Wei China
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Citations per field, relative to Leonardo Galteri
Leonardo Galteri · 1×
Citations per year, relative to Leonardo Galteri
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Countries citing papers authored by Leonardo Galteri

Since Specialization
Citations

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

Fields of papers citing papers by Leonardo Galteri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonardo Galteri

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Galteri. A scholar is included among the top collaborators of Leonardo Galteri 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 Leonardo Galteri. Leonardo Galteri 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
# Work Indexed citations
1 22
2 5
3 3
4 2
5 1
6 6
7 76
8 2
9 9
10 17
11 4
12 10
13 58
14
Coarse-to-Fine 3D Face Reconstruction
2
15 20
16 261
17 12
18 37
19 24
20 130

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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