Pilar Sobrevilla

1.2k total citations · 1 hit paper
42 papers, 792 citations indexed

About

Pilar Sobrevilla is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Biomedical Engineering. According to data from OpenAlex, Pilar Sobrevilla has authored 42 papers receiving a total of 792 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 8 papers in Media Technology and 7 papers in Biomedical Engineering. Recurrent topics in Pilar Sobrevilla's work include Image Retrieval and Classification Techniques (16 papers), Medical Image Segmentation Techniques (13 papers) and Color Science and Applications (6 papers). Pilar Sobrevilla is often cited by papers focused on Image Retrieval and Classification Techniques (16 papers), Medical Image Segmentation Techniques (13 papers) and Color Science and Applications (6 papers). Pilar Sobrevilla collaborates with scholars based in Spain, United States and Italy. Pilar Sobrevilla's 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 Miquel E. Cabañas and has published in prestigious journals such as Fuzzy Sets and Systems, Magnetic Resonance Imaging and Color Research & Application.

In The Last Decade

Pilar Sobrevilla

38 papers receiving 743 citations

Hit Papers

State of the art survey on MRI brain tumor segmentation 2013 2026 2017 2021 2013 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. State of the art survey on MRI brain tumor segmentation
    2013 464 citations Nelly Gordillo, E. Montseny et al. Magnetic Resonance Imaging profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pilar Sobrevilla Spain 11 507 364 150 143 117 42 792
E. Montseny Spain 9 481 0.9× 369 1.0× 144 1.0× 130 0.9× 59 0.5× 40 698
Martin Silbiger United States 9 714 1.4× 357 1.0× 280 1.9× 213 1.5× 53 0.5× 10 1.0k
Gil J. Ettinger United States 12 381 0.8× 85 0.2× 62 0.4× 173 1.2× 155 1.3× 30 698
Cem Direkoğlu Türkiye 14 576 1.1× 373 1.0× 339 2.3× 142 1.0× 73 0.6× 40 935
Tongxue Zhou China 14 519 1.0× 399 1.1× 257 1.7× 307 2.1× 117 1.0× 25 886
Catherine Garbay France 14 378 0.7× 92 0.3× 206 1.4× 91 0.6× 39 0.3× 50 691
S. Deepak India 10 757 1.5× 903 2.5× 586 3.9× 336 2.3× 102 0.9× 37 1.4k
Yuqi Fang China 14 151 0.3× 66 0.2× 218 1.5× 224 1.6× 44 0.4× 44 712
Gongning Luo China 16 279 0.6× 143 0.4× 139 0.9× 247 1.7× 119 1.0× 58 716
Hideki Nakayama Japan 16 702 1.4× 113 0.3× 650 4.3× 234 1.6× 65 0.6× 78 1.2k

Countries citing papers authored by Pilar Sobrevilla

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Avilés-Rivero, Angelica I., et al.. (2016). A Deep-Neuro-Fuzzy approach for estimating the interaction forces in Robotic surgery. 1113–1119. 32 indexed citations
2.
Avilés-Rivero, Angelica I., et al.. (2015). Automatic and robust single-camera specular highlight removal in cardiac images. PubMed. 2015. 675–8. 19 indexed citations
3.
Avilés-Rivero, Angelica I., Pilar Sobrevilla, & Alı́cia Casals. (2014). Unconstrained &#x2113;<inf>1</inf> &#x2014; regularized minimization with interpolated transformations for heart motion compensation. PubMed. 41. 5109–5112. 4 indexed citations
4.
Gordillo, Nelly, E. Montseny, & Pilar Sobrevilla. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging. 31(8). 1426–1438. 464 indexed citations breakdown →
5.
Cabañas, Miquel E., et al.. (2011). Decision tree based fuzzy classifier of magnetic resonance spectra from cerebrospinal fluid samples. Fuzzy Sets and Systems. 170(1). 43–63. 8 indexed citations
6.
7.
Romaní, Santiago, Pilar Sobrevilla, & E. Montseny. (2011). Variability estimation of hue and saturation components in the HSV space. Color Research & Application. 37(4). 261–271. 8 indexed citations
8.
Sobrevilla, Pilar, et al.. (2010). Filtering false detections of small multiple sclerosis lesions using fuzzy regional analysis. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 19. 1–8. 1 indexed citations
9.
Sobrevilla, Pilar, et al.. (2010). FUZZY-BASED ANALYSIS OF MICROSCOPIC COLOR CERVICAL PAP SMEAR IMAGES: NUCLEI DETECTION. International Journal of Computational Intelligence and Applications. 9(3). 187–206. 10 indexed citations
10.
11.
Grau-Sánchez, Miquel, E. Montseny, & Pilar Sobrevilla. (2007). On the Use of Fuzzy Texture Spectrum for Homogeneous and Textured Images Discrimination. Proceedings of ... IEEE International Conference on Fuzzy Systems. 1. 1–6. 1 indexed citations
12.
Montseny, E., et al.. (2006). Fuzzy Texture Unit and Fuzzy Texture Spectrum for texture characterization. Fuzzy Sets and Systems. 158(3). 239–252. 20 indexed citations
13.
Sobrevilla, Pilar, et al.. (2006). Robustness and Performance Evaluation of the Fuzzy Texture Spectrum encoding. 56. 1279–1286. 4 indexed citations
14.
Montseny, E., et al.. (2005). On the Fuzzy Texture Spectrum for Natural Microtextures Characterization.. European Society for Fuzzy Logic and Technology Conference. 685–690. 5 indexed citations
15.
Sobrevilla, Pilar, Daniel Gómez, Javier Montero, & E. Montseny. (2005). Unsupervised Perceptual Model for Color Image's Segmentation. 349–354.
16.
Montseny, E., Pilar Sobrevilla, & Santiago Romaní. (2005). A fuzzy approach to white blood cells segmentation in color bone marrow images. 1. 173–178. 25 indexed citations
17.
Sobrevilla, Pilar, Enrique Lerma, & E. Montseny. (2004). An approach to a Fuzzy-based Automatic Pap Screening System-FAPSS-addressed to cytology cells detection. 101. 138–142. 2 indexed citations
18.
Montseny, E., et al.. (2003). Edge orientation-based fuzzy Hough transform (EOFHT).. European Society for Fuzzy Logic and Technology Conference. 683–688. 2 indexed citations
19.
Sobrevilla, Pilar, E. Montseny, & James M. Keller. (2003). White blood cell detection in bone marrow images. 403–407. 29 indexed citations
20.
Montseny, E. & Pilar Sobrevilla. (2001). On fuzzy rule-based algorithms for image segmentation using gray-level histogram analysis.. European Society for Fuzzy Logic and Technology Conference. 59–62. 5 indexed citations

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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