E. Montseny
- Computer Vision and Pattern Recognition top 2%
- Neurology top 2%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Media Technology top 5%
- Co-authors
- Pilar SobrevillaNelly GordilloSantiago RomaníJames M. KellerAlı́cia CasalsAngelica I. Avilés-RiveroEnrique LermaÀlex Rovira
- Topics
- Image Retrieval and Classification Techniques (16 papers)Medical Image Segmentation Techniques (15 papers)Remote-Sensing Image Classification (5 papers)
- Partner nations
- SpainUnited StatesItaly
In The Last Decade
E. Montseny
36 papers receiving 650 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Computer Vision and Pattern Recognition 481
- Neurology 369
- Artificial Intelligence 144
- Radiology, Nuclear Medicine and Imaging 130
- Media Technology 67
Countries citing papers authored by E. Montseny
This map shows the geographic impact of E. Montseny'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 E. Montseny with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. Montseny more than expected).
Fields of papers citing papers by E. Montseny
This network shows the impact of papers produced by E. Montseny. 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 E. Montseny. The network helps show where E. Montseny may publish in the future.
Co-authorship network of co-authors of E. Montseny
This figure shows the co-authorship network connecting the top 25 collaborators of E. Montseny. A scholar is included among the top collaborators of E. Montseny 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 E. Montseny. E. Montseny is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 32 | |
| 3 | 4 | |
| 4 | State of the art survey on MRI brain tumor segmentationbreakdown → | 464 |
| 5 | 4 | |
| 6 | 8 | |
| 7 | 1 | |
| 8 | 10 | |
| 9 | 6 | |
| 10 | 1 | |
| 11 | 20 | |
| 12 | 4 | |
| 13 | On the Fuzzy Texture Spectrum for Natural Microtextures Characterization. | 5 |
| 14 | 0 | |
| 15 | 25 | |
| 16 | 2 | |
| 17 | Edge orientation-based fuzzy Hough transform (EOFHT). | 2 |
| 18 | 29 | |
| 19 | On fuzzy rule-based algorithms for image segmentation using gray-level histogram analysis. | 5 |
| 20 | Computer vision: specialized processors for real-time image analysis : workshop proceedings, Barcelona, Spain, September 1991 | 1 |
About E. Montseny
E. Montseny is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Biophysics, having authored 40 papers that have together received 698 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (16 papers), Medical Image Segmentation Techniques (15 papers) and Remote-Sensing Image Classification (5 papers). The work is most often cited by research in Neurology (369 citations), Computer Vision and Pattern Recognition (481 citations) and Biophysics (50 citations). E. Montseny has collaborated with scholars based in Spain, United States and Italy. Frequent co-authors include Pilar Sobrevilla, Nelly Gordillo, Santiago Romaní, James M. Keller, Alı́cia Casals, Angelica I. Avilés-Rivero, Enrique Lerma, Àlex Rovira, Luís Ortega and J. Frau. Their work appears in journals such as Fuzzy Sets and Systems, Magnetic Resonance Imaging and Biomedical Signal Processing and Control.
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.