F. Cesarini

594 citations
33 papers · 428 · h-index 12

Impact in

    • Handwritten Text Recognition Techniques
    • Image Retrieval and Classification Techniques
    • Advanced Image and Video Retrieval Techniques
    • Image Processing and 3D Reconstruction
    • Semantic Web and Ontologies
    • Text and Document Classification Technologies

Papers in

F. Cesarini

27 papers receiving 394 citations

Peers

F. Cesarini
Comparison fields: 5 of 38
  • Computer Vision and Pattern Recognition 347
  • Artificial Intelligence 131
  • Media Technology 33
  • Signal Processing 40
  • Information Systems 58
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Ning-San Chang United States
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Citations per field
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Citations per year

Countries citing papers authored by F. Cesarini

Since Specialization
Citations

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

Fields of papers citing papers by F. Cesarini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside F. Cesarini, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with F. Cesarini Line = papers co-authored together F. Cesarini links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200371
2 199868
3 199935
4 200233
5 200132
6 200331
7 200227
8 200421
9 199616
10 200216
11 199615
12 199911
13 20027
14 20027
15 20026
16 20016
17 19824
18 19834
19 19913
20 20002

About F. Cesarini

F. Cesarini is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Information Systems, having authored 33 papers that have together received 428 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (11 papers), Advanced Database Systems and Queries (10 papers), Handwritten Text Recognition Techniques (9 papers), Data Management and Algorithms (8 papers), Algorithms and Data Compression (7 papers), Semantic Web and Ontologies (7 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Web Data Mining and Analysis (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (347 citations), Artificial Intelligence (131 citations), Media Technology (33 citations), Signal Processing (40 citations) and Information Systems (58 citations). F. Cesarini has collaborated with scholars based in Italy, Germany and Japan. Frequent co-authors include G. Soda, Simone Marinai, Marco Gori, Lorenzo Sarti, Enrico Francesconi, Jenny Sheng, Alessandro Artale, A.M. Colla, Michelangelo Diligenti and Emanuele Marino. Their work appears in journals such as International Journal on Document Analysis and Recognition (IJDAR), Lecture notes in computer science, Information Systems, Data & Knowledge Engineering and ACM Transactions on Database Systems.

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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