Tomás Brandão

667 total citations · 1 hit paper
27 papers, 408 citations indexed

About

Tomás Brandão is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Plant Science. According to data from OpenAlex, Tomás Brandão has authored 27 papers receiving a total of 408 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 4 papers in Signal Processing and 3 papers in Plant Science. Recurrent topics in Tomás Brandão's work include Advanced Steganography and Watermarking Techniques (7 papers), Advanced Data Compression Techniques (6 papers) and Image and Video Quality Assessment (6 papers). Tomás Brandão is often cited by papers focused on Advanced Steganography and Watermarking Techniques (7 papers), Advanced Data Compression Techniques (6 papers) and Image and Video Quality Assessment (6 papers). Tomás Brandão collaborates with scholars based in Portugal and Norway. Tomás Brandão's co-authors include Maria Paula Queluz, João C. Ferreira, Pedro Santana, António Rodrigues, Susana Marta Almeida, Leonel Sousa, Paulo Chaves, Márcia L. Baptista, Tiago M. F. Alves and Luís Brás Rosário and has published in prestigious journals such as IEEE Access, International Journal of Environmental Research and Public Health and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Tomás Brandão

23 papers receiving 391 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.

Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey

2022 117 citations Tomás Brandão, João C. Ferreira et al. Agriculture profile →

Peers

Tomás Brandão

CVPR

PS

MT

SP

ECOLO

Divya Meena Sundaram India

Olarik Surinta Thailand

Fábio A. Faria Brazil

Sergio Sánchez Colombia

Romain Raveaux France

Jiawei Yan China

Jinyu Zuo United States

Ahlad Kumar India

Yifei Xu China

Yogesh Dandawate India

Divya Meena Sundaram India

Tomás Brandão

91 ×0.4

CVPR

49 ×0.4

PS

136 ×1.8

MT

21 ×0.4

SP

77 ×1.7

ECOLO

Citations per year, relative to Tomás Brandão Tomás Brandão (= 1×) peers Divya Meena Sundaram

Countries citing papers authored by Tomás Brandão

Since Specialization

Citations

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

Fields of papers citing papers by Tomás Brandão

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomás Brandão

This figure shows the co-authorship network connecting the top 25 collaborators of Tomás Brandão. A scholar is included among the top collaborators of Tomás Brandão 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 Tomás Brandão. Tomás Brandão 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.

Brandão, Tomás, et al.. (2024). Wildfire Detection With Deep Learning—A Case Study for the CICLOPE Project. IEEE Access. 12. 82095–82110. 6 indexed citations

2.

Elvas, Luís B., et al.. (2024). Deep learning for automatic calcium detection in echocardiography. BioData Mining. 17(1). 27–27. 2 indexed citations

3.

Brandão, Tomás, et al.. (2023). Multi-Camera Person Re-Identification Based on Trajectory Data. Applied Sciences. 13(20). 11578–11578. 1 indexed citations

4.

Alves, Tiago M. F., et al.. (2023). On the Use of Feedforward Neural Networks to Improve the Intercore Crosstalk Tolerance in Self-Coherent MCF Systems. 26. 1–4. 1 indexed citations

5.

Brandão, Tomás, et al.. (2023). An Educational Game to Teach Children about Air Quality Using Augmented Reality and Tangible Interaction with Sensors. International Journal of Environmental Research and Public Health. 20(5). 3814–3814. 6 indexed citations

6.

Brandão, Tomás, et al.. (2022). Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning. Agriculture. 12(11). 1967–1967. 11 indexed citations

7.

Brandão, Tomás, et al.. (2022). Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey. Agriculture. 12(9). 1350–1350. 117 indexed citations breakdown →

8.

Mariano, Pedro, et al.. (2022). An Information System for Air Quality Monitoring using Mobile Sensor Networks. 238–246. 2 indexed citations

9.

Alves, Tiago M. F., et al.. (2022). Short-Reach MCF-Based Systems Employing KK Receivers and Feedforward Neural Networks for ICXT Mitigation. Photonics. 9(5). 286–286. 1 indexed citations

10.

Baptista, Márcia L., et al.. (2019). Improved real-time wildfire detection using a surveillance system. World Congress on Engineering. 4 indexed citations

11.

Brandão, Tomás, et al.. (2012). Bitstream-based quality metric for packetized transmission of H.264 encoded video. International Conference on Systems, Signals and Image Processing. 312–315. 5 indexed citations

12.

Brandão, Tomás & Maria Paula Queluz. (2010). No-Reference Quality Assessment of H.264/AVC Encoded Video. IEEE Transactions on Circuits and Systems for Video Technology. 20(11). 1437–1447. 74 indexed citations

13.

Brandão, Tomás, et al.. (2009). Quality assessment of H.264/AVC encoded video. 0–0. 3 indexed citations

14.

Brandão, Tomás & Maria Paula Queluz. (2007). No-reference image quality assessment based on DCT domain statistics. Signal Processing. 88(4). 822–833. 110 indexed citations

15.

Brandão, Tomás & Maria Paula Queluz. (2007). Blind Perceptual Quality Assessment Method For Dct-Based Encoded Images. Zenodo (CERN European Organization for Nuclear Research). 154–158. 2 indexed citations

16.

Sousa, Leonel, et al.. (2007). Meta-Pipeline: A New Execution Mechanism for Distributed Pipeline Processing. 23. 5–5. 3 indexed citations

17.

Brandão, Tomás. (2005). Image communication quality assessment based on watermarking, an overview.. 4 indexed citations

18.

Queluz, Maria Paula, Tomás Brandão, & António Rodrigues. (2004). Signal combining techniques for video watermarking extraction. 347–350.

19.

Brandão, Tomás & António Rodrigues. (2001). Improving Spread Spectrum Based Image Watermarking Through Non-binary Channel Coding. 3 indexed citations

20.

Brandão, Tomás, Maria Paula Queluz, & António Rodrigues. (2001). <title>Performance improvement of spatial watermarking through efficient nonbinary channel coding</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4314. 651–662. 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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