Miguel Cazorla

3.0k total citations
136 papers, 1.7k citations indexed

About

Miguel Cazorla is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Miguel Cazorla has authored 136 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Computer Vision and Pattern Recognition, 48 papers in Aerospace Engineering and 20 papers in Artificial Intelligence. Recurrent topics in Miguel Cazorla's work include Robotics and Sensor-Based Localization (48 papers), Advanced Image and Video Retrieval Techniques (27 papers) and Advanced Vision and Imaging (23 papers). Miguel Cazorla is often cited by papers focused on Robotics and Sensor-Based Localization (48 papers), Advanced Image and Video Retrieval Techniques (27 papers) and Advanced Vision and Imaging (23 papers). Miguel Cazorla collaborates with scholars based in Spain, Panama and United States. Miguel Cazorla's co-authors include Sergio Orts‐Escolano, José García‐Rodríguez, Francisco Gomez‐Donoso, Ester Martínez-Martín, Vicente Morell, Alberto García-García, Jorge Azorín-López, Francisco Escolano, Boyán Bonev and Jesús Martínez-Gómez and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Miguel Cazorla

125 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Miguel Cazorla Spain 22 775 327 261 199 195 136 1.7k
Senem Velipasalar United States 27 1.2k 1.5× 167 0.5× 380 1.5× 146 0.7× 342 1.8× 195 2.8k
John Zelek Canada 19 922 1.2× 339 1.0× 262 1.0× 116 0.6× 105 0.5× 153 1.5k
Jesse S. Jin Australia 25 1.2k 1.6× 161 0.5× 241 0.9× 185 0.9× 79 0.4× 126 2.3k
Tiziana D’Orazio Italy 27 1.1k 1.4× 267 0.8× 229 0.9× 93 0.5× 311 1.6× 115 2.2k
Lap-Fai Yu United States 24 1.2k 1.5× 275 0.8× 124 0.5× 314 1.6× 82 0.4× 82 2.4k
Hema Swetha Koppula United States 14 1.3k 1.6× 239 0.7× 665 2.5× 132 0.7× 181 0.9× 21 2.1k
Jin Xie China 31 1.9k 2.4× 317 1.0× 427 1.6× 368 1.8× 127 0.7× 117 2.8k
Hongkai Wen United Kingdom 21 748 1.0× 671 2.1× 267 1.0× 102 0.5× 151 0.8× 67 1.8k
Caroline Pantofaru United States 19 1.4k 1.8× 217 0.7× 466 1.8× 67 0.3× 133 0.7× 37 2.2k
Horst–Michael Groß Germany 22 856 1.1× 324 1.0× 314 1.2× 80 0.4× 421 2.2× 130 2.1k

Countries citing papers authored by Miguel Cazorla

Since Specialization
Citations

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

Fields of papers citing papers by Miguel Cazorla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miguel Cazorla

This figure shows the co-authorship network connecting the top 25 collaborators of Miguel Cazorla. A scholar is included among the top collaborators of Miguel Cazorla 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 Miguel Cazorla. Miguel Cazorla 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.
Gomez‐Donoso, Francisco, et al.. (2025). CORRECTNESS OF CODE EVALUATION AND IMPROVEMENT USING LARGE LANGUAGE MODELS. INTED proceedings. 1. 1348–1352. 1 indexed citations
2.
Roig-Vila, Rosabel, María Paz Prendes Espinosa, & Miguel Cazorla. (2025). Implementation of Artificial Intelligence Technologies for the Assessment of Students’ Attentional State: A Scoping Review. Applied Sciences. 15(11). 5990–5990.
3.
Gomez‐Donoso, Francisco, et al.. (2024). Holograms for seamless integration of remote students in the classroom. Virtual Reality. 28(1). 3 indexed citations
4.
Cazorla, Miguel, et al.. (2023). Social robotics in music education: A systematic review. Frontiers in Education. 8. 6 indexed citations
5.
Cazorla, Miguel, et al.. (2023). Experimental Analysis of the Effectiveness of a Cyber-physical Robotic System to Assist Speech and Language Pathologists in High School. Journal of New Approaches in Educational Research. 12(1). 40–61. 1 indexed citations
6.
Cazorla, Miguel, et al.. (2023). Towards a Better Performance in Facial Expression Recognition: A Data‐Centric Approach. Computational Intelligence and Neuroscience. 2023(1). 1394882–1394882. 5 indexed citations
7.
Cazorla, Miguel, et al.. (2023). A Large Visual, Qualitative, and Quantitative Dataset for Web Intelligence Applications. Computational Intelligence and Neuroscience. 2023(1). 3 indexed citations
8.
Cazorla, Miguel, et al.. (2023). Improving Facial Expression Recognition Through Data Preparation and Merging. IEEE Access. 11. 71339–71360. 1 indexed citations
9.
Gomez‐Donoso, Francisco, et al.. (2022). Three-dimensional reconstruction using SFM for actual pedestrian classification. Expert Systems with Applications. 213. 119006–119006. 10 indexed citations
10.
Gomez‐Donoso, Francisco, et al.. (2021). Accurate Multilevel Classification for Wildlife Images. Computational Intelligence and Neuroscience. 2021(1). 6690590–6690590. 3 indexed citations
11.
Gomez‐Donoso, Francisco, et al.. (2021). A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot. Applied Sciences. 11(7). 2943–2943. 5 indexed citations
12.
Gomez‐Donoso, Francisco, et al.. (2020). A Voxelized Fractal Descriptor for 3D Object Recognition. IEEE Access. 8. 161958–161968. 8 indexed citations
13.
Martínez-Martín, Ester, et al.. (2020). Socially Assistive Robots for Older Adults and People with Autism: An Overview. Electronics. 9(2). 367–367. 61 indexed citations
14.
Gomez‐Donoso, Francisco, et al.. (2020). Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views. Applied Sciences. 10(10). 3409–3409. 8 indexed citations
15.
Gomez‐Donoso, Francisco, et al.. (2019). Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot. Computational Intelligence and Neuroscience. 2019. 1–15. 18 indexed citations
16.
Cazorla, Miguel, et al.. (2019). Refining the Fusion of Pepper Robot and Estimated Depth Maps Method for Improved 3D Perception. IEEE Access. 7. 185076–185085. 3 indexed citations
17.
Martínez-Martín, Ester & Miguel Cazorla. (2019). Rehabilitation Technology: Assistance from Hospital to Home. Computational Intelligence and Neuroscience. 2019. 1–8. 17 indexed citations
18.
Cazorla, Miguel, et al.. (2018). Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged. Computational Intelligence and Neuroscience. 2018. 1–17. 6 indexed citations
19.
González, Germán, et al.. (2018). On the Relevance of the Loss Function in the Agatston Score Regression from Non-ECG Gated CT Scans. Lecture notes in computer science. 11040. 326–334. 3 indexed citations
20.
Iglesia-Vayá, María de la, José María Salinas, Gonzalo Rojas, et al.. (2015). BIMCV: Synergy between Peta Bytes of data in population medical imaging, computer aided diagnosis and AVR.. 987–989. 3 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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