Javier Romero

73 total papers · 1.5k total citations
58 papers, 1.1k citations indexed

About

Javier Romero is a scholar working on Atomic and Molecular Physics, and Optics, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Javier Romero has authored 58 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Atomic and Molecular Physics, and Optics, 25 papers in Cognitive Neuroscience and 24 papers in Computer Vision and Pattern Recognition. Recurrent topics in Javier Romero's work include Color Science and Applications (47 papers), Visual perception and processing mechanisms (22 papers) and Image Enhancement Techniques (18 papers). Javier Romero is often cited by papers focused on Color Science and Applications (47 papers), Visual perception and processing mechanisms (22 papers) and Image Enhancement Techniques (18 papers). Javier Romero collaborates with scholars based in Spain, United States and Iran. Javier Romero's co-authors include Javier Hernández‐Andrés, Raymond L. Lee, Juan L. Nieves, Eva M. Valero, E. Hita, F.J. Brenes Bermúdez, José Antonio García García, F.J. Olmo, Sérgio Nascimento and Luis Jiménez del Barco and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Image Processing and Sensors.

In The Last Decade

Javier Romero

55 papers receiving 1.0k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Javier Romero 663 321 228 193 190 58 1.1k
Javier Hernández‐Andrés 687 1.0× 335 1.0× 256 1.1× 192 1.0× 205 1.1× 78 1.4k
Alan Robertson 630 1.0× 280 0.9× 285 1.3× 76 0.4× 366 1.9× 35 1.5k
Scott Henderson 510 0.8× 197 0.6× 289 1.3× 54 0.3× 274 1.4× 57 1.4k
Raymond L. Lee 377 0.6× 144 0.4× 135 0.6× 43 0.2× 119 0.6× 34 1.0k
William A. Thornton 793 1.2× 150 0.5× 403 1.8× 51 0.3× 467 2.5× 91 1.4k
M. J. Vrhel 626 0.9× 579 1.8× 139 0.6× 183 0.9× 157 0.8× 46 1.1k
Yoshi Ohno 555 0.8× 141 0.4× 172 0.8× 22 0.1× 282 1.5× 51 1.3k
Wendy Davis 488 0.7× 146 0.5× 197 0.9× 33 0.2× 307 1.6× 75 1.4k
Francisco M. Martínez‐Verdú 368 0.6× 145 0.5× 95 0.4× 45 0.2× 127 0.7× 102 947
F. J. J. Clarke 314 0.5× 98 0.3× 317 1.4× 32 0.2× 163 0.9× 42 1.0k

Countries citing papers authored by Javier Romero

Since Specialization
Citations

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

Fields of papers citing papers by Javier Romero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Javier Romero

This figure shows the co-authorship network connecting the top 25 collaborators of Javier Romero. A scholar is included among the top collaborators of Javier Romero 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 Javier Romero. Javier Romero is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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