Pedro H. Bugatti

677 total citations
36 papers, 335 citations indexed

About

Pedro H. Bugatti is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Pedro H. Bugatti has authored 36 papers receiving a total of 335 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 7 papers in Molecular Biology. Recurrent topics in Pedro H. Bugatti's work include Image Retrieval and Classification Techniques (16 papers), Advanced Image and Video Retrieval Techniques (15 papers) and Smart Agriculture and AI (6 papers). Pedro H. Bugatti is often cited by papers focused on Image Retrieval and Classification Techniques (16 papers), Advanced Image and Video Retrieval Techniques (15 papers) and Smart Agriculture and AI (6 papers). Pedro H. Bugatti collaborates with scholars based in Brazil and Argentina. Pedro H. Bugatti's co-authors include Priscila T. M. Saito, Agma J. M. Traina, Caetano Traina, Alexandre Rossi Paschoal, Douglas Silva Domingues, Wonder Alexandre Luz Alves, Marcela Xavier Ribeiro, Paulo Mazzoncini de Azevedo‐Marques, Fabrício Martins Lopes and Joaquim Cezar Felipe and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Industrial Electronics.

In The Last Decade

Pedro H. Bugatti

35 papers receiving 317 citations

Peers

Pedro H. Bugatti

CVPR

MB

AI

PS

CR

Priscila T. M. Saito Brazil

M. Manohar India

Haixia Long China

Xiaoying Qiu China

Junkang Zhang China

Minhui Wang China

Kangkana Bora India

Lin Luo China

Ruiling Liu China

Bin Xia China

Priscila T. M. Saito Brazil

Pedro H. Bugatti

73 ×0.5

CVPR

86 ×1.0

MB

95 ×1.2

AI

68 ×1.0

PS

39 ×1.0

CR

Citations per year, relative to Pedro H. Bugatti Pedro H. Bugatti (= 1×) peers Priscila T. M. Saito

Countries citing papers authored by Pedro H. Bugatti

Since Specialization

Citations

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

Fields of papers citing papers by Pedro H. Bugatti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pedro H. Bugatti. 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 Pedro H. Bugatti. The network helps show where Pedro H. Bugatti may publish in the future.

Co-authorship network of co-authors of Pedro H. Bugatti

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

All Works

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20 of 20 papers shown

1.

Corrêa, Cléber Gimenez, et al.. (2024). Automatic generation of difficulty maps for datasets using neural network. Multimedia Tools and Applications. 83(25). 66499–66516.

2.

Saito, Priscila T. M., et al.. (2023). Soybean seed vigor classification through an effective image learning-based approach. Multimedia Tools and Applications. 83(5). 13113–13136. 4 indexed citations

3.

Oliveira, Eduardo Rolim de, Pedro H. Bugatti, & Priscila T. M. Saito. (2023). Assessment of clustering techniques to support the analyses of soybean seed vigor. PLoS ONE. 18(8). e0285566–e0285566. 2 indexed citations

4.

Bugatti, Pedro H., et al.. (2021). Method for selecting representative videos for change detection datasets. Multimedia Tools and Applications. 81(3). 3773–3791. 1 indexed citations

5.

Domingues, Douglas Silva, et al.. (2020). TERL: classification of transposable elements by convolutional neural networks. Briefings in Bioinformatics. 22(3). 32 indexed citations

6.

Bugatti, Pedro H., et al.. (2020). Active semi-supervised learning for biological data classification. PLoS ONE. 15(8). e0237428–e0237428. 20 indexed citations

7.

Bugatti, Pedro H., et al.. (2020). Assessing Active Learning Strategies to Improve the Quality Control of the Soybean Seed Vigor. IEEE Transactions on Industrial Electronics. 68(2). 1675–1683. 6 indexed citations

8.

Bugatti, Pedro H., et al.. (2019). Breast cancer diagnosis through active learning in content-based image retrieval. Neurocomputing. 357. 1–10. 26 indexed citations

9.

Bugatti, Pedro H., et al.. (2019). Contributing to agriculture by using soybean seed data from the tetrazolium test. SHILAP Revista de lepidopterología. 23. 103652–103652. 14 indexed citations

10.

Alves, Wonder Alexandre Luz, et al.. (2018). Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants. Briefings in Bioinformatics. 20(2). 682–689. 48 indexed citations

11.

Bugatti, Pedro H., et al.. (2017). Towards an Effective and Efficient Learning for Biomedical Data Classification. 415. 13–18. 1 indexed citations

12.

Saito, Priscila T. M., et al.. (2016). An image analysis framework for effective classification of seed damages. 61–66. 5 indexed citations

13.

Saito, Priscila T. M., et al.. (2015). Exploiting Evolutionary Approaches for Content-Based Medical Image Retrieval. 370–371. 2 indexed citations

14.

Saito, Priscila T. M., et al.. (2014). Content-based image retrieval towards the automatic characterization of soybean seed vigor. 964–969. 4 indexed citations

15.

Bugatti, Pedro H., et al.. (2013). PRoSPer: Perceptual similarity queries in medical CBIR systems through user profiles. Computers in Biology and Medicine. 45. 8–19. 10 indexed citations

16.

Bugatti, Pedro H., et al.. (2013). Does a CBIR system really impact decisions of physicians in a clinical environment?. 41–46. 14 indexed citations

17.

Bugatti, Pedro H., et al.. (2011). Integrating user profile in medical CBIR systems to answer perceptual similarity queries. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7963. 79630Q–79630Q. 1 indexed citations

18.

Bugatti, Pedro H., et al.. (2010). FMI-SiR: A Flexible and Efficient Module for Similarity Searching on Oracle Database. Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais). 1(2). 229–244. 7 indexed citations

19.

Bugatti, Pedro H., et al.. (2009). Incorporating Metric Access Methods for Similarity Searching on Oracle Database.. 196–210. 1 indexed citations

20.

Bugatti, Pedro H., et al.. (2009). Content-based retrieval of medical images: From context to perception. 6414. 1–8. 8 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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