Sergiu Oprea

3.0k total citations · 2 hit papers
13 papers, 2.1k citations indexed

About

Sergiu Oprea is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Artificial Intelligence. According to data from OpenAlex, Sergiu Oprea has authored 13 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 5 papers in Human-Computer Interaction and 3 papers in Artificial Intelligence. Recurrent topics in Sergiu Oprea's work include Human Pose and Action Recognition (7 papers), Advanced Neural Network Applications (5 papers) and Hand Gesture Recognition Systems (5 papers). Sergiu Oprea is often cited by papers focused on Human Pose and Action Recognition (7 papers), Advanced Neural Network Applications (5 papers) and Hand Gesture Recognition Systems (5 papers). Sergiu Oprea collaborates with scholars based in Spain, United Kingdom and Greece. Sergiu Oprea's co-authors include José García‐Rodríguez, Alberto García-García, Sergio Orts‐Escolano, Víctor Villena-Martínez, Pablo Martínez-González, John Alejandro Castro-Vargas, Antonis Argyros, Jorge Azorín-López, Miguel Cazorla and Marcelo Saval-Calvo and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Soft Computing and Pattern Recognition Letters.

In The Last Decade

Sergiu Oprea

12 papers receiving 2.0k citations

Hit Papers

A survey on deep learning techniques for image and video ... 2017 2026 2020 2023 2018 2017 250 500 750 1000
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. A survey on deep learning techniques for image and video semantic segmentation
    2018 1.1k citations Alberto García-García, Sergio Orts‐Escolano et al. Applied Soft Computing profile →
  2. A Review on Deep Learning Techniques Applied to Semantic Segmentation
    2017 742 citations Alberto García-García, Sergio Orts‐Escolano et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergiu Oprea Spain 8 959 378 263 218 174 13 2.1k
Víctor Villena-Martínez Spain 5 869 0.9× 343 0.9× 252 1.0× 213 1.0× 171 1.0× 7 1.9k
Alberto García-García Spain 15 1.1k 1.2× 421 1.1× 311 1.2× 228 1.0× 183 1.1× 34 2.6k
Sergio Orts‐Escolano Spain 21 1.6k 1.6× 514 1.4× 382 1.5× 243 1.1× 193 1.1× 67 3.3k
Jingbo Wang China 10 1.6k 1.6× 507 1.3× 144 0.5× 318 1.5× 136 0.8× 22 2.1k
Changqian Yu China 11 1.8k 1.8× 657 1.7× 154 0.6× 349 1.6× 140 0.8× 18 2.4k
Filippo Attivissimo Italy 29 405 0.4× 251 0.7× 206 0.8× 159 0.7× 149 0.9× 160 2.5k
Zheng-Ning Liu China 7 1.3k 1.3× 583 1.5× 114 0.4× 379 1.7× 218 1.3× 10 2.5k
Meng-Hao Guo China 7 1.3k 1.3× 591 1.6× 117 0.4× 382 1.8× 221 1.3× 23 2.6k
Xuebin Qin China 15 1.9k 2.0× 423 1.1× 249 0.9× 526 2.4× 254 1.5× 36 3.2k
Tai‐Jiang Mu China 18 1.6k 1.7× 568 1.5× 164 0.6× 323 1.5× 180 1.0× 56 2.8k

Countries citing papers authored by Sergiu Oprea

Since Specialization
Citations

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

Fields of papers citing papers by Sergiu Oprea

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergiu Oprea

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

All Works

13 of 13 papers shown
1.
Oprea, Sergiu, et al.. (2022). Predicting Human-Object Interactions in Egocentric Videos. 2022 International Joint Conference on Neural Networks (IJCNN). 1–7. 3 indexed citations
2.
Martínez-González, Pablo, et al.. (2022). Synthetic contact maps to predict grasp regions on objects. 2022 International Joint Conference on Neural Networks (IJCNN). 9351. 1–6. 1 indexed citations
3.
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
4.
Villena-Martínez, Víctor, Sergiu Oprea, Marcelo Saval-Calvo, et al.. (2020). When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs). Applied Sciences. 10(21). 7524–7524. 24 indexed citations
5.
García‐Rodríguez, José, Francisco Gomez‐Donoso, Sergiu Oprea, et al.. (2019). COMBAHO: A deep learning system for integrating brain injury patients in society. Pattern Recognition Letters. 137. 80–90. 2 indexed citations
6.
Oprea, Sergiu, Pablo Martínez-González, Alberto García-García, et al.. (2019). A visually realistic grasping system for object manipulation and interaction in virtual reality environments. Computers & Graphics. 83. 77–86. 26 indexed citations
7.
García-García, Alberto, Sergio Orts‐Escolano, Sergiu Oprea, et al.. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing. 70. 41–65. 1061 indexed citations breakdown →
8.
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
9.
García-García, Alberto, Sergio Orts‐Escolano, Sergiu Oprea, Víctor Villena-Martínez, & José García‐Rodríguez. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv (Cornell University). 742 indexed citations breakdown →
10.
Oprea, Sergiu, Alberto García-García, José García‐Rodríguez, Sergio Orts‐Escolano, & Miguel Cazorla. (2017). A recurrent neural network based Schaeffer gesture recognition system. 15. 425–431. 5 indexed citations
11.
García-García, Alberto, et al.. (2017). Classifying Behaviours in Videos with Recurrent Neural Networks. Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante). 7(4). 1–15. 8 indexed citations
12.
Oprea, Sergiu, et al.. (2017). A long short‐term memory based Schaeffer gesture recognition system. Expert Systems. 35(2). 4 indexed citations
13.
García-García, Alberto, Sergio Orts‐Escolano, Sergiu Oprea, et al.. (2016). Multi-sensor 3D object dataset for object recognition with full pose estimation. Neural Computing and Applications. 28(5). 941–952. 9 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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