Pilar Sobrevilla
- Computer Vision and Pattern Recognition top 2%
- Neurology top 2%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Biomedical Engineering
- Co-authors
- E. MontsenyNelly GordilloAlı́cia CasalsAngelica I. Avilés-RiveroSantiago RomaníJames M. KellerJames K. HahnEnrique Lerma
- Topics
- Image Retrieval and Classification Techniques (16 papers)Medical Image Segmentation Techniques (13 papers)Color Science and Applications (6 papers)
- Partner nations
- SpainUnited StatesItaly
In The Last Decade
Pilar Sobrevilla
38 papers receiving 743 citations
Hit Papers
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 507
- Neurology 364
- Artificial Intelligence 150
- Radiology, Nuclear Medicine and Imaging 143
- Biomedical Engineering 117
Countries citing papers authored by Pilar Sobrevilla
This map shows the geographic impact of Pilar Sobrevilla'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 Pilar Sobrevilla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pilar Sobrevilla more than expected).
Fields of papers citing papers by Pilar Sobrevilla
This network shows the impact of papers produced by Pilar Sobrevilla. 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 Pilar Sobrevilla. The network helps show where Pilar Sobrevilla may publish in the future.
Co-authorship network of co-authors of Pilar Sobrevilla
This figure shows the co-authorship network connecting the top 25 collaborators of Pilar Sobrevilla. A scholar is included among the top collaborators of Pilar Sobrevilla 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 Pilar Sobrevilla. Pilar Sobrevilla is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 32 | |
| 2 | 8 | |
| 3 | 19 | |
| 4 | 4 | |
| 5 | State of the art survey on MRI brain tumor segmentationbreakdown → | 464 |
| 6 | 4 | |
| 7 | 8 | |
| 8 | 8 | |
| 9 | 1 | |
| 10 | 10 | |
| 11 | 6 | |
| 12 | 20 | |
| 13 | 4 | |
| 14 | On the Fuzzy Texture Spectrum for Natural Microtextures Characterization. | 5 |
| 15 | 0 | |
| 16 | 25 | |
| 17 | 2 | |
| 18 | Edge orientation-based fuzzy Hough transform (EOFHT). | 2 |
| 19 | 29 | |
| 20 | On fuzzy rule-based algorithms for image segmentation using gray-level histogram analysis. | 5 |
About Pilar Sobrevilla
Pilar Sobrevilla is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Biophysics, having authored 42 papers that have together received 792 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (16 papers), Medical Image Segmentation Techniques (13 papers) and Color Science and Applications (6 papers). The work is most often cited by research in Neurology (364 citations), Computer Vision and Pattern Recognition (507 citations) and Biophysics (50 citations). Pilar Sobrevilla has collaborated with scholars based in Spain, United States and Italy. Frequent co-authors include E. Montseny, Nelly Gordillo, Alı́cia Casals, Angelica I. Avilés-Rivero, Santiago Romaní, James M. Keller, James K. Hahn, Enrique Lerma, Àlex Rovira and Manuel Comabella. Their work appears in journals such as Fuzzy Sets and Systems, Magnetic Resonance Imaging and Color Research & Application.
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.