Salvatore Tabbone

3.2k total citations · 1 hit paper
81 papers, 1.6k citations indexed

About

Salvatore Tabbone is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Salvatore Tabbone has authored 81 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 7 papers in Media Technology. Recurrent topics in Salvatore Tabbone's work include Image Retrieval and Classification Techniques (45 papers), Advanced Image and Video Retrieval Techniques (36 papers) and Image Processing and 3D Reconstruction (21 papers). Salvatore Tabbone is often cited by papers focused on Image Retrieval and Classification Techniques (45 papers), Advanced Image and Video Retrieval Techniques (36 papers) and Image Processing and 3D Reconstruction (21 papers). Salvatore Tabbone collaborates with scholars based in France, Vietnam and Spain. Salvatore Tabbone's co-authors include Djemel Ziou, Laurent Wendling, Thai V. Hoang, Akrem Sellami, Ernest Valveny, Oriol Ramos Terrades, Karl Tombre, Salim Jouili, Ricardo da Silva Torres and Siome Goldenstein and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Salvatore Tabbone

74 papers receiving 1.5k citations

Hit Papers

Edge Detection Techniques... 1998 2026 2007 2016 1998 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Salvatore Tabbone France 19 1.2k 311 217 105 101 81 1.6k
Qunsheng Peng China 26 1.3k 1.2× 251 0.8× 89 0.4× 105 1.0× 81 0.8× 197 2.2k
Tomáš Suk Czechia 21 1.9k 1.6× 504 1.6× 171 0.8× 204 1.9× 81 0.8× 56 2.5k
Lifeng He China 18 1.1k 1.0× 344 1.1× 287 1.3× 141 1.3× 26 0.3× 105 2.0k
Xiaoyi Dong China 11 1.0k 0.9× 252 0.8× 504 2.3× 136 1.3× 38 0.4× 26 1.6k
Fernand S. Cohen United States 21 1.4k 1.2× 325 1.0× 193 0.9× 182 1.7× 62 0.6× 88 1.9k
Yanwen Guo China 22 1.9k 1.6× 516 1.7× 703 3.2× 101 1.0× 125 1.2× 119 2.8k
S.M. Yamany United States 12 1.4k 1.2× 523 1.7× 317 1.5× 225 2.1× 87 0.9× 27 1.8k
Bo Peng China 22 748 0.6× 222 0.7× 332 1.5× 291 2.8× 67 0.7× 147 1.7k
Yinjie Lei China 27 1.4k 1.2× 267 0.9× 477 2.2× 200 1.9× 208 2.1× 71 2.0k
Theo Pavlidis United States 19 1.1k 1.0× 215 0.7× 193 0.9× 90 0.9× 35 0.3× 48 1.5k

Countries citing papers authored by Salvatore Tabbone

Since Specialization
Citations

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

Fields of papers citing papers by Salvatore Tabbone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Salvatore Tabbone

This figure shows the co-authorship network connecting the top 25 collaborators of Salvatore Tabbone. A scholar is included among the top collaborators of Salvatore Tabbone 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 Salvatore Tabbone. Salvatore Tabbone 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
1.
Tabbone, Salvatore, et al.. (2024). Historical Postcards Retrieval through Vision Foundation Models. 50–56.
2.
Werneck, Rafael de Oliveira, Romain Raveaux, Salvatore Tabbone, & Ricardo da Silva Torres. (2019). Learning cost function for graph classification with open-set methods. Pattern Recognition Letters. 128. 8–15. 2 indexed citations
3.
Ziou, Djemel, et al.. (2019). A novel correlation filter based on variational calculus. Signal Processing Image Communication. 78. 77–85. 1 indexed citations
4.
Hoang, Thai V., Elisa H. Barney Smith, & Salvatore Tabbone. (2013). Sparsity-based edge noise removal from bilevel graphical document images. International Journal on Document Analysis and Recognition (IJDAR). 17(2). 161–179. 12 indexed citations
5.
Hoang, Thai V. & Salvatore Tabbone. (2011). Invariant pattern recognition using the RFM descriptor. Pattern Recognition. 45(1). 271–284. 41 indexed citations
6.
Hoang, Thai V. & Salvatore Tabbone. (2011). Generic polar harmonic transforms for invariant image description. 829–832. 18 indexed citations
7.
Hoang, Thai V. & Salvatore Tabbone. (2011). Generic R-transform for invariant pattern representation. HAL (Le Centre pour la Communication Scientifique Directe). 157–162. 1 indexed citations
8.
Visani, Muriel, Oriol Ramos Terrades, & Salvatore Tabbone. (2010). A protocol to characterize the descriptive power and the complementarity of shape descriptors. International Journal on Document Analysis and Recognition (IJDAR). 14(1). 87–100.
9.
Jouili, Salim, Mickaël Coustaty, Salvatore Tabbone, & Jean-Marc Ogier. (2010). NAVIDOMASS: Structural-based Approaches Towards Handling Historical Documents. HAL (Le Centre pour la Communication Scientifique Directe). 946–949. 6 indexed citations
10.
Terrades, Oriol Ramos, Ernest Valveny, & Salvatore Tabbone. (2008). Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(9). 1630–1644. 54 indexed citations
11.
Tabbone, Salvatore, et al.. (2007). A Bayesian classifier for symbol recognition. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations
12.
Terrades, Oriol Ramos, Salvatore Tabbone, & Ernest Valveny. (2007). Optimal Linear Combination for Two-Class Classifiers.
13.
Tabbone, Salvatore, et al.. (2006). Une méthode de localisation et de reconnaissance de symboles sans connaissance a priori. HAL (Le Centre pour la Communication Scientifique Directe). 127–131. 1 indexed citations
14.
Tombre, Karl, Salvatore Tabbone, & Philippe Dosch. (2005). Musings on Symbol Recognition. HAL (Le Centre pour la Communication Scientifique Directe).
15.
Wendling, Laurent, et al.. (2005). Automatical definition of measures from the combination of shape descriptors. 2886. 986–990 Vol. 2. 1 indexed citations
16.
Wendling, Laurent & Salvatore Tabbone. (2004). A new way to detect arrows in line drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(7). 935–941. 12 indexed citations
17.
Tabbone, Salvatore & Laurent Wendling. (2004). Binarization of color images from an adaptation of possibilistic c-means algorithm. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.. 704–707 Vol.1. 2 indexed citations
18.
Tabbone, Salvatore & Djemel Ziou. (2002). Efficient edge detection using two scales. 789–790. 3 indexed citations
19.
Ziou, Djemel & Salvatore Tabbone. (1998). Edge Detection Techniques-An Overview. HAL (Le Centre pour la Communication Scientifique Directe). 8(4). 537–559. 537 indexed citations breakdown →
20.
Tabbone, Salvatore, et al.. (1996). An approach to detect lofar lines. Pattern Recognition Letters. 17(1). 37–46. 14 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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