Diego Bertolini

1.1k total citations · 1 hit paper
28 papers, 679 citations indexed

About

Diego Bertolini is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Diego Bertolini has authored 28 papers receiving a total of 679 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 3 papers in Information Systems. Recurrent topics in Diego Bertolini's work include Handwritten Text Recognition Techniques (9 papers), Image Retrieval and Classification Techniques (4 papers) and Digital Media Forensic Detection (4 papers). Diego Bertolini is often cited by papers focused on Handwritten Text Recognition Techniques (9 papers), Image Retrieval and Classification Techniques (4 papers) and Digital Media Forensic Detection (4 papers). Diego Bertolini collaborates with scholars based in Brazil, Canada and Italy. Diego Bertolini's co-authors include Luiz S. Oliveira, Yandre M. G. Costa, Lucas Teixeira, Robert Sabourin, Rodolfo M. Pereira, Carlos N. Silla, Edson Justino, George D. C. Cavalcanti, Alceu S. Britto and Alexandre M. Amory and has published in prestigious journals such as Expert Systems with Applications, Sensors and Pattern Recognition.

In The Last Decade

Diego Bertolini

20 papers receiving 647 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

2020 328 citations Rodolfo M. Pereira, Diego Bertolini et al. Computer Methods and Programs in Biomedicine profile →

Peers

Diego Bertolini

AI

CVPR

RNMI

MT

HI

Subhankar Ghosh India

Christoph Linse Germany

Vasundhara Acharya India

Tej Bahadur Chandra India

Muhammad Attique Pakistan

Hritam Basak India

Shailendra Tiwari India

Neha Gianchandani Canada

He Sui China

V. N. Manjunath Aradhya India

Subhankar Ghosh India

Diego Bertolini

258 ×0.7

AI

116 ×0.3

CVPR

202 ×0.7

RNMI

16 ×0.1

MT

40 ×0.7

HI

Citations per year, relative to Diego Bertolini Diego Bertolini (= 1×) peers Subhankar Ghosh

Countries citing papers authored by Diego Bertolini

Since Specialization

Citations

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

Fields of papers citing papers by Diego Bertolini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Diego Bertolini

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Teixeira, Lucas, Diego Bertolini, Luiz S. Oliveira, George D. C. Cavalcanti, & Yandre M. G. Costa. (2025). Improving open set recognition with dissimilarity-based metric learning. Knowledge-Based Systems. 327. 114108–114108.

2.

Teixeira, Lucas, et al.. (2025). Contrastive Dissimilarity for Writer Identification: a proof of concept. 1–5.

3.

Bertolini, Diego, et al.. (2024). A Database for Soybean Seed Classification. 37. 1–6. 1 indexed citations

4.

Bertolini, Diego, et al.. (2024). Pipelines Leak Localization Through Acoustic Emission and Deep Learning. 1–4.

5.

Bertolini, Diego, et al.. (2023). Predição do Conceito ENADE dos Cursos de Computação no Brasil. 1305–1316.

6.

Costa, Yandre M. G., Lucas Teixeira, Rodolfo M. Pereira, et al.. (2022). COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. Sensors. 22(19). 7303–7303. 9 indexed citations

7.

Bertolini, Diego, et al.. (2022). Análise do Desempenho no Enade dos Concluintes de Computação usando Técnica de Agrupamento. 834–845.

8.

Pereira, Rodolfo M., Diego Bertolini, Lucas Teixeira, Carlos N. Silla, & Yandre M. G. Costa. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine. 194. 105532–105532. 328 indexed citations breakdown →

9.

Bertolini, Diego, et al.. (2020). Braille character detection using deep neural networks for an educational robot for visually impaired people. 123–128. 6 indexed citations

10.

Bertolini, Diego, et al.. (2019). Offline Handwritten Script Recognition Based on Texture Descriptors. 57–62.

11.

Costa, Yandre M. G., Diego Bertolini, Alceu S. Britto, George D. C. Cavalcanti, & Luiz S. Oliveira. (2019). The dissimilarity approach: a review. Artificial Intelligence Review. 53(4). 2783–2808. 13 indexed citations

12.

Pereira, Rodolfo M., et al.. (2018). A Resampling Approach for Imbalanceness on Music Genre Classification using Spectrograms.. The Florida AI Research Society. 500–505. 3 indexed citations

13.

Bertolini, Diego, et al.. (2018). A Dataset for Electromyography-Based Dactylology Recognition. 2376–2381. 3 indexed citations

14.

Costa, Yandre M. G., et al.. (2018). Bird species identification using spectrogram and dissimilarity approach. Ecological Informatics. 48. 187–197. 27 indexed citations

15.

Bertolini, Diego, et al.. (2017). Off-line writer identification using handcrafted features versus ConvNets. 20. 1–8. 2 indexed citations

16.

Costa, Yandre M. G., et al.. (2016). Music genre classification using visual features with feature selection. 1–6. 2 indexed citations

17.

Bertolini, Diego, Luiz S. Oliveira, Edson Justino, & Robert Sabourin. (2014). Assessing Textural Features for Writer Identification on Different Writing Styles and Forgeries. 5. 268–272. 1 indexed citations

18.

Bertolini, Diego, Luiz S. Oliveira, Edson Justino, & Robert Sabourin. (2012). Texture-based descriptors for writer identification and verification. Expert Systems with Applications. 40(6). 2069–2080. 115 indexed citations

19.

Bertolini, Diego, Luiz S. Oliveira, Edson Justino, & Robert Sabourin. (2009). Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognition. 43(1). 387–396. 144 indexed citations

20.

Bertolini, Diego, Luiz S. Oliveira, Edson Justino, & Robert Sabourin. (2008). Ensemble of classifiers for off-line signature verification. Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics. 20. 283–288. 3 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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