Ferran Diego

496 citations
23 papers · 272 indexed · h-index 11
Topics
Advanced Vision and Imaging (11 papers)Video Surveillance and Tracking Methods (7 papers)Robotics and Sensor-Based Localization (4 papers)
Partner nations
SpainGermanyGreece

In The Last Decade

Ferran Diego

21 papers receiving 257 citations

Peers

Ferran Diego
Comparison fields: 5 of 63
  • Computer Vision and Pattern Recognition 131
  • Automotive Engineering 60
  • Artificial Intelligence 54
  • Computer Networks and Communications 44
  • Signal Processing 38
Replace Muhammad Zeeshan Khan with:
Muhammad Zeeshan Khan Pakistan
Tomasz Marciniak Poland
Wen-Huang Cheng Taiwan
Arnaldo J. Abrantes Portugal
Jasem Almotiri Saudi Arabia
Hadi Kazemi United States
Samy Bakheet Egypt
Ahmed B. Altamimi Saudi Arabia
Alexander Gepperth Germany
Ferran Diego relative to Muhammad Zeeshan Khan Pakistan Muhammad Zeeshan Khan's profile →
Citations per field
00.5×10×13×
Muhammad Zeeshan Khan · 1×
Citations per year

Countries citing papers authored by Ferran Diego

Since Specialization
Citations

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

Fields of papers citing papers by Ferran Diego

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ferran Diego

This figure shows the co-authorship network connecting the top 25 collaborators of Ferran Diego. A scholar is included among the top collaborators of Ferran Diego 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 Ferran Diego. Ferran Diego 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
#WorkIndexed citations
1 27
2 1
3 5
4 19
5 40
6
Cost efficient gradient boosting
26
7
Sparse convolutional coding for neuronal assembly detection
4
8 7
9 4
10 19
11 20
12 2
13 21
14 29
15 0
16 1
17
Video alignment for difference-spotting
5
18 3
19 3
20
Voice Conversion of Non-aligned Data using Unit Selection
15

About Ferran Diego

Ferran Diego is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Media Technology, having authored 23 papers that have together received 272 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (11 papers), Video Surveillance and Tracking Methods (7 papers) and Robotics and Sensor-Based Localization (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (131 citations), Automotive Engineering (60 citations) and Signal Processing (38 citations). Ferran Diego has collaborated with scholars based in Spain, Germany and Greece. Frequent co-authors include Antonio M. López, Joan Serrat, Fred A. Hamprecht, Daniel Ponsa, José M. Alvarez, Boaz Nadler, Björn Ommer, Lucas Drumond, Miguel Ángel Bautista and Antonio Bonafonte. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and IEEE Transactions on Intelligent Transportation Systems.

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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