Adrián Peñate-Sánchez

444 total citations
22 papers, 275 citations indexed

About

Adrián Peñate-Sánchez is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Computational Mechanics. According to data from OpenAlex, Adrián Peñate-Sánchez has authored 22 papers receiving a total of 275 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 9 papers in Aerospace Engineering and 4 papers in Computational Mechanics. Recurrent topics in Adrián Peñate-Sánchez's work include Robotics and Sensor-Based Localization (9 papers), Advanced Vision and Imaging (6 papers) and Human Pose and Action Recognition (4 papers). Adrián Peñate-Sánchez is often cited by papers focused on Robotics and Sensor-Based Localization (9 papers), Advanced Vision and Imaging (6 papers) and Human Pose and Action Recognition (4 papers). Adrián Peñate-Sánchez collaborates with scholars based in Spain, United Kingdom and Italy. Adrián Peñate-Sánchez's co-authors include Francesc Moreno-Noguer, Juan Andrade‐Cetto, Christopher Zach, Minh-Tri Pham, Maurice Fallon, Lorenzo Porzi, Elisa Ricci, Javier Lorenzo-Navarro, Lourdes Agapito and Samuel Rota Bulò and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Aquaculture.

In The Last Decade

Adrián Peñate-Sánchez

20 papers receiving 264 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adrián Peñate-Sánchez Spain 10 189 171 45 41 27 22 275
Chule Yang Singapore 10 198 1.0× 198 1.2× 36 0.8× 19 0.5× 40 1.5× 24 310
Tristan Laidlow United Kingdom 6 280 1.5× 146 0.9× 40 0.9× 77 1.9× 19 0.7× 8 370
Binbin Xu United Kingdom 8 179 0.9× 162 0.9× 34 0.8× 65 1.6× 22 0.8× 22 268
Antoni Rosinol United States 4 185 1.0× 200 1.2× 28 0.6× 51 1.2× 28 1.0× 5 281
Michael Warren Australia 9 183 1.0× 189 1.1× 25 0.6× 24 0.6× 25 0.9× 23 252
Patrick Sayd France 11 415 2.2× 258 1.5× 22 0.5× 74 1.8× 30 1.1× 20 490
Daniel Herrera C. Finland 2 254 1.3× 152 0.9× 21 0.5× 49 1.2× 15 0.6× 3 319
Chris Sweeney United States 13 327 1.7× 231 1.4× 32 0.7× 68 1.7× 21 0.8× 20 437
Philipp Lindenberger Switzerland 4 291 1.5× 205 1.2× 17 0.4× 58 1.4× 11 0.4× 4 386
Yuncheng Lu Singapore 4 205 1.1× 226 1.3× 20 0.4× 24 0.6× 57 2.1× 22 327

Countries citing papers authored by Adrián Peñate-Sánchez

Since Specialization
Citations

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

Fields of papers citing papers by Adrián Peñate-Sánchez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adrián Peñate-Sánchez. 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 Adrián Peñate-Sánchez. The network helps show where Adrián Peñate-Sánchez may publish in the future.

Co-authorship network of co-authors of Adrián Peñate-Sánchez

This figure shows the co-authorship network connecting the top 25 collaborators of Adrián Peñate-Sánchez. A scholar is included among the top collaborators of Adrián Peñate-Sánchez 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 Adrián Peñate-Sánchez. Adrián Peñate-Sánchez 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.
Garcés, Elena, et al.. (2024). IReNe: Instant Recoloring of Neural Radiance Fields. Acceda (Universidad de Las Palmas de Gran Canaria). 5937–5946.
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Peñate-Sánchez, Adrián, et al.. (2023). A machine learning approach to design a DPSIR model: A real case implementation of evidence-based policy creation using AI. Advanced Engineering Informatics. 57. 102042–102042. 2 indexed citations
4.
Peñate-Sánchez, Adrián, et al.. (2023). NeRFLight: Fast and Light Neural Radiance Fields using a Shared Feature Grid. QRU Quaderns de Recerca en Urbanisme. 12417–12427. 1 indexed citations
5.
Phillips, Alexander B., et al.. (2023). Developing a Reconfigurable Architecture for the Remote Operation of Marine Autonomous Systems. IEEE Software. 41(4). 160–170. 2 indexed citations
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Peñate-Sánchez, Adrián, et al.. (2022). Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs. Animals. 12(14). 1755–1755. 1 indexed citations
8.
Freire-Obregón, David, et al.. (2021). Improving user verification in human-robot interaction from audio or image inputs through sample quality assessment. Pattern Recognition Letters. 149. 179–184. 9 indexed citations
9.
Peñate-Sánchez, Adrián, et al.. (2021). TGCRBNW: A Dataset for Runner Bib Number Detection (and Recognition) in the Wild. Acceda (Universidad de Las Palmas de Gran Canaria). 9445–9451. 2 indexed citations
10.
Peñate-Sánchez, Adrián, et al.. (2020). TGC20ReId: A dataset for sport event re-identification in the wild. Pattern Recognition Letters. 138. 355–361. 10 indexed citations
11.
Peñate-Sánchez, Adrián, et al.. (2019). SKD: Unsupervised Keypoint Detecting for Point Clouds using Embedded Saliency Estimation.. arXiv (Cornell University). 1 indexed citations
12.
Peñate-Sánchez, Adrián, et al.. (2019). Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU. IEEE Robotics and Automation Letters. 4(2). 1327–1334. 31 indexed citations
13.
Peñate-Sánchez, Adrián, et al.. (2018). Detect Globally, Label Locally: Learning Accurate 6-DOF Object Pose Estimation by Joint Segmentation and Coordinate Regression. IEEE Robotics and Automation Letters. 3(4). 3960–3967. 12 indexed citations
14.
Porzi, Lorenzo, Adrián Peñate-Sánchez, Elisa Ricci, & Francesc Moreno-Noguer. (2017). Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 5777–5783. 10 indexed citations
15.
Porzi, Lorenzo, Samuel Rota Bulò, Adrián Peñate-Sánchez, Elisa Ricci, & Francesc Moreno-Noguer. (2016). Learning Depth-Aware Deep Representations for Robotic Perception. IEEE Robotics and Automation Letters. 2(2). 468–475. 21 indexed citations
16.
Peñate-Sánchez, Adrián, Lorenzo Porzi, & Francesc Moreno-Noguer. (2015). Matchability Prediction for Full-Search Template Matching Algorithms. QRU Quaderns de Recerca en Urbanisme. 353–361. 10 indexed citations
17.
Villamizar, Michael, Luis Ferraz, Adrián Peñate-Sánchez, et al.. (2015). Efficient monocular pose estimation for complex 3D models. Acceda (Universidad de Las Palmas de Gran Canaria). 1397–1402. 16 indexed citations
18.
Peñate-Sánchez, Adrián, Francesc Moreno-Noguer, Juan Andrade‐Cetto, & François Fleuret. (2014). LETHA: Learning from High Quality Inputs for 3D Pose Estimation in Low Quality Images. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 9. 517–524. 2 indexed citations
19.
Peñate-Sánchez, Adrián, Juan Andrade‐Cetto, & Francesc Moreno-Noguer. (2013). Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(10). 2387–2400. 100 indexed citations
20.
Peñate-Sánchez, Adrián, et al.. (2013). Simultaneous Pose, Focal Length and 2D-to-3D Correspondences from Noisy Observations. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 81.1–81.11. 1 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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