Pau Riba
Impact in
-
- Handwritten Text Recognition Techniques
- Image Processing and 3D Reconstruction
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Signal Processing top 10%
- Music and Audio Processing
Papers in
-
- Handwritten Text Recognition Techniques 14
- Advanced Image and Video Retrieval Techniques 11
- Graph Theory and Algorithms 9
- Image Processing and 3D Reconstruction 5
-
- Advanced Graph Neural Networks 6
- Natural Language Processing Techniques 6
- Topic Modeling 5
- Co-authors
- Alícia Fornés (29 shared papers)Josep Lladós (19 shared papers)Mauricio Villegas (7 shared papers)Marçal Rusiñol (6 shared papers)Lei Kang (6 shared papers)Anjan Dutta (7 shared papers)Oriol Ramos Terrades (2 shared papers)Lutz Goldmann (2 shared papers)
In The Last Decade
Pau Riba
30 papers receiving 563 citations
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 502
- Signal Processing 88
- Artificial Intelligence 238
- Media Technology 52
- Music 13
Countries citing papers authored by Pau Riba
This map shows the geographic impact of Pau Riba'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 Pau Riba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pau Riba more than expected).
Fields of papers citing papers by Pau Riba
This network shows the impact of papers produced by Pau Riba. 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 Pau Riba. The network helps show where Pau Riba may publish in the future.
Co-authors
The 18 scholars most cited alongside Pau Riba, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 80 | |
| 2 | 2020 | 71 | |
| 3 | 2019 | 59 | |
| 4 | 2019 | 46 | |
| 5 | 2019 | 44 | |
| 6 | 2021 | 44 | |
| 7 | 2021 | 28 | |
| 8 | 2015 | 27 | |
| 9 | 2021 | 26 | |
| 10 | 2020 | 25 | |
| 11 | 2018 | 18 | |
| 12 | 2022 | 17 | |
| 13 | 2016 | 15 | |
| 14 | 2019 | 13 | |
| 15 | 2018 | 10 | |
| 16 | 2016 | 9 | |
| 17 | 2020 | 9 | |
| 18 | 2017 | 7 | |
| 19 | 2017 | 6 | |
| 20 | 2019 | 6 |
About Pau Riba
Pau Riba is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Information Systems and Communication, having authored 30 papers that have together received 593 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (14 papers), Advanced Image and Video Retrieval Techniques (11 papers), Graph Theory and Algorithms (9 papers), Music and Audio Processing (7 papers), Advanced Graph Neural Networks (6 papers), Natural Language Processing Techniques (6 papers), Topic Modeling (5 papers) and Image Processing and 3D Reconstruction (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (502 citations), Signal Processing (88 citations), Artificial Intelligence (238 citations), Media Technology (52 citations) and Music (13 citations). Pau Riba has collaborated with scholars based in Spain, Canada and China. Frequent co-authors include Alícia Fornés, Josep Lladós, Mauricio Villegas, Marçal Rusiñol, Lei Kang, Anjan Dutta, Oriol Ramos Terrades, Lutz Goldmann, Jorge Calvo-Zaragoza and Yaxing Wang. Their work appears in journals such as Pattern Recognition, Pattern Recognition Letters, Neural Computing and Applications, International Journal on Document Analysis and Recognition (IJDAR) and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.