Fernando Díaz-de-María
- Signal Processing top 2%
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Physiology
- Experimental and Cognitive Psychology
- Co-authors
- Eduardo Martínez-EnríquezIván González-DíazCarmen Peláez-MorenoAscensión Gallardo-AntolínAmaya Jiménez-MorenoJuan Ignacio Godino-LlorentePatricia HenríquezCarlos M. Travieso
- Topics
- Video Coding and Compression Technologies (28 papers)Advanced Vision and Imaging (22 papers)Advanced Data Compression Techniques (20 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Signal ProcessingExpert Systems with Applications
- Partner nations
- SpainUnited StatesGermany
In The Last Decade
Fernando Díaz-de-María
88 papers receiving 756 citations
Peers
Comparison fields: 5 of 99
- Signal Processing 449
- Computer Vision and Pattern Recognition 430
- Artificial Intelligence 319
- Physiology 74
- Experimental and Cognitive Psychology 49
Countries citing papers authored by Fernando Díaz-de-María
This map shows the geographic impact of Fernando Díaz-de-María'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 Fernando Díaz-de-María with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Díaz-de-María more than expected).
Fields of papers citing papers by Fernando Díaz-de-María
This network shows the impact of papers produced by Fernando Díaz-de-María. 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 Fernando Díaz-de-María. The network helps show where Fernando Díaz-de-María may publish in the future.
Co-authorship network of co-authors of Fernando Díaz-de-María
This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Díaz-de-María. A scholar is included among the top collaborators of Fernando Díaz-de-María 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 Fernando Díaz-de-María. Fernando Díaz-de-María is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 7 | |
| 7 | 9 | |
| 8 | 2 | |
| 9 | 3 | |
| 10 | 11 | |
| 11 | 32 | |
| 12 | 20 | |
| 13 | 8 | |
| 14 | 9 | |
| 15 | 18 | |
| 16 | 1 | |
| 17 | 30 | |
| 18 | 4 | |
| 19 | Support Vector Machines for continuous speech recognition | 20 |
| 20 | Some experiments on speaker-independent isolated digit recognition using SVM classifiers. | 5 |
About Fernando Díaz-de-María
Fernando Díaz-de-María is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 92 papers that have together received 810 indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (28 papers), Advanced Vision and Imaging (22 papers) and Advanced Data Compression Techniques (20 papers). The work is most often cited by research in Signal Processing (449 citations), Computer Vision and Pattern Recognition (430 citations) and Artificial Intelligence (319 citations). Fernando Díaz-de-María has collaborated with scholars based in Spain, United States and Germany. Frequent co-authors include Eduardo Martínez-Enríquez, Iván González-Díaz, Carmen Peláez-Moreno, Ascensión Gallardo-Antolín, Amaya Jiménez-Moreno, Juan Ignacio Godino-Llorente, Patricia Henríquez, Carlos M. Travieso, Jesús B. Alonso and Miguel A. Ferrer. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing and Expert Systems with Applications.
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.