Sergio Orts‐Escolano

5.8k total citations · 2 hit papers
67 papers, 3.3k citations indexed

About

Sergio Orts‐Escolano is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Sergio Orts‐Escolano has authored 67 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Computer Vision and Pattern Recognition, 21 papers in Aerospace Engineering and 10 papers in Artificial Intelligence. Recurrent topics in Sergio Orts‐Escolano's work include Robotics and Sensor-Based Localization (21 papers), Advanced Vision and Imaging (20 papers) and Video Surveillance and Tracking Methods (13 papers). Sergio Orts‐Escolano is often cited by papers focused on Robotics and Sensor-Based Localization (21 papers), Advanced Vision and Imaging (20 papers) and Video Surveillance and Tracking Methods (13 papers). Sergio Orts‐Escolano collaborates with scholars based in Spain, United Kingdom and United States. Sergio Orts‐Escolano's co-authors include José García‐Rodríguez, Alberto García-García, Sergiu Oprea, Víctor Villena-Martínez, Pablo Martínez-González, Miguel Cazorla, Francisco Gomez‐Donoso, Vicente Morell, Jorge Azorín-López and John Alejandro Castro-Vargas and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Expert Systems with Applications and Sensors.

In The Last Decade

Sergio Orts‐Escolano

65 papers receiving 3.2k citations

Hit Papers

A survey on deep learning techniques for image and video ... 2017 2026 2020 2023 2018 2017 250 500 750 1000

Peers

Sergio Orts‐Escolano
Sergio Orts‐Escolano
Citations per year, relative to Sergio Orts‐Escolano Sergio Orts‐Escolano (= 1×) peers José García‐Rodríguez

Countries citing papers authored by Sergio Orts‐Escolano

Since Specialization
Citations

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

Fields of papers citing papers by Sergio Orts‐Escolano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sergio Orts‐Escolano. 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 Sergio Orts‐Escolano. The network helps show where Sergio Orts‐Escolano may publish in the future.

Co-authorship network of co-authors of Sergio Orts‐Escolano

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Orts‐Escolano. A scholar is included among the top collaborators of Sergio Orts‐Escolano 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 Sergio Orts‐Escolano. Sergio Orts‐Escolano 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.
Collins, Emily, Abhimitra Meka, Franziska Mueller, et al.. (2024). GANtlitz: Ultra High Resolution Generative Model for Multi‐Modal Face Textures. Computer Graphics Forum. 43(2). 2 indexed citations
2.
Du, Ruofei, Michelle Mohr Carney, Yinda Zhang, et al.. (2023). Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual Programming. 1–23. 18 indexed citations
3.
Sarkar, Kripasindhu, Sergio Orts‐Escolano, Dmitry Lagun, et al.. (2023). Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis. 3379–3390. 4 indexed citations
4.
Meka, Abhimitra, Rohit Pandey, Christian Häne, et al.. (2020). Deep Relightable Textures Volumetric Performance Capture with Neural Rendering. MPG.PuRe (Max Planck Society). 36 indexed citations
5.
Oprea, Sergiu, Pablo Martínez-González, Alberto García-García, et al.. (2020). A Review on Deep Learning Techniques for Video Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(6). 2806–2826. 167 indexed citations
6.
Orts‐Escolano, Sergio, et al.. (2020). An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques. Sensors. 20(22). 6451–6451. 60 indexed citations
7.
Angelopoulou, Anastassia, José García‐Rodríguez, Sergio Orts‐Escolano, et al.. (2019). Evaluation of different chrominance models in the detection and reconstruction of faces and hands using the growing neural gas network. Pattern Analysis and Applications. 22(4). 1667–1685. 7 indexed citations
8.
Gomez‐Donoso, Francisco, et al.. (2019). UASOL, a large-scale high-resolution outdoor stereo dataset. Scientific Data. 6(1). 162–162. 11 indexed citations
9.
Gomez‐Donoso, Francisco, Sergio Orts‐Escolano, & Miguel Cazorla. (2019). Accurate and efficient 3D hand pose regression for robot hand teleoperation using a monocular RGB camera. Expert Systems with Applications. 136. 327–337. 33 indexed citations
10.
Cazorla, Miguel, et al.. (2018). A New Dataset and Performance Evaluation of a Region-Based CNN for Urban Object Detection. Electronics. 7(11). 301–301. 20 indexed citations
11.
García-García, Alberto, José García‐Rodríguez, Sergio Orts‐Escolano, et al.. (2017). A study of the effect of noise and occlusion on the accuracy of convolutional neural networks applied to 3D object recognition. Computer Vision and Image Understanding. 164. 124–134. 14 indexed citations
12.
Loop, Charles, Qin Cai, Sergio Orts‐Escolano, & Philip A. Chou. (2016). A Closed-Form Bayesian Fusion Equation Using Occupancy Probabilities. 380–388. 9 indexed citations
13.
Angelopoulou, Anastassia, José García‐Rodríguez, Sergio Orts‐Escolano, Gaurav Gupta, & Αλεξάνδρα Ψαρρού. (2016). Fast 2D/3D object representation with growing neural gas. Neural Computing and Applications. 29(10). 903–919. 13 indexed citations
14.
Morell, Vicente, et al.. (2016). Object recognition in noisy RGB-D data using GNG. Pattern Analysis and Applications. 20(4). 1061–1076. 10 indexed citations
15.
García-García, Alberto, Sergio Orts‐Escolano, José García‐Rodríguez, & Miguel Cazorla. (2016). Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors. Journal of Real-Time Image Processing. 14(3). 585–604. 12 indexed citations
16.
Azorín-López, Jorge, Marcelo Saval-Calvo, Andrés Fuster-Guilló, José García‐Rodríguez, & Sergio Orts‐Escolano. (2015). Self-Organizing Activity Description Map to represent and classify human behaviour. 1–7. 5 indexed citations
17.
Boom, Bastiaan J., et al.. (2015). Interactive light source position estimation for augmented reality with an RGB‐D camera. Computer Animation and Virtual Worlds. 28(1). 14 indexed citations
18.
García‐Rodríguez, José, Anastassia Angelopoulou, Juan Manuel García‐Chamizo, et al.. (2012). Autonomous Growing Neural Gas for applications with time constraint: Optimal parameter estimation. Neural Networks. 32. 196–208. 21 indexed citations
19.
Orts‐Escolano, Sergio, et al.. (2012). GPGPU implementation of growing neural gas: Application to 3D scene reconstruction. Journal of Parallel and Distributed Computing. 72(10). 1361–1372. 10 indexed citations
20.
Lopez‐Llorca, Luis V. & Sergio Orts‐Escolano. (1994). Histopathology of infection of the palm Washingtonia filifera with the pink bud rot fungus Penicillium vermoesenii. Mycological Research. 98(10). 1195–1199. 8 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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