Mariantonia Cotronei

524 total citations
37 papers, 325 citations indexed

About

Mariantonia Cotronei is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Signal Processing. According to data from OpenAlex, Mariantonia Cotronei has authored 37 papers receiving a total of 325 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 18 papers in Computational Mechanics and 9 papers in Signal Processing. Recurrent topics in Mariantonia Cotronei's work include Image and Signal Denoising Methods (19 papers), Advanced Numerical Analysis Techniques (18 papers) and Digital Filter Design and Implementation (9 papers). Mariantonia Cotronei is often cited by papers focused on Image and Signal Denoising Methods (19 papers), Advanced Numerical Analysis Techniques (18 papers) and Digital Filter Design and Implementation (9 papers). Mariantonia Cotronei collaborates with scholars based in Italy, Germany and United States. Mariantonia Cotronei's co-authors include Luigia Puccio, Laura Montefusco, Tomas Sauer, Costanza Conti, Damiana Lazzaro, Antonella Molinaro, Massimo Condoluci, Antonio Iera, Giuseppe Araniti and Thomas Sauer and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Mariantonia Cotronei

33 papers receiving 309 citations

Peers

Mariantonia Cotronei
Comparison fields: 5 of 44
  • Computer Vision and Pattern Recognition 174
  • Computational Mechanics 121
  • Signal Processing 71
  • Mechanical Engineering 59
  • Media Technology 54
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Citations per field, relative to Mariantonia Cotronei
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Citations per year, relative to Mariantonia Cotronei
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Countries citing papers authored by Mariantonia Cotronei

Since Specialization
Citations

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

Fields of papers citing papers by Mariantonia Cotronei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mariantonia Cotronei

This figure shows the co-authorship network connecting the top 25 collaborators of Mariantonia Cotronei. A scholar is included among the top collaborators of Mariantonia Cotronei 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 Mariantonia Cotronei. Mariantonia Cotronei 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 0
2 0
3 2
4 0
5 4
6 0
7 3
8 2
9 9
10 13
11 15
12 4
13 10
14 21
15 1
16 2
17
A new class of non-separable symmetric wavelets for image processing
4
18 3
19 3
20 3

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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