C. Sanz

1.1k total citations
77 papers, 596 citations indexed

About

C. Sanz is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology. According to data from OpenAlex, C. Sanz has authored 77 papers receiving a total of 596 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Computer Vision and Pattern Recognition, 32 papers in Signal Processing and 23 papers in Media Technology. Recurrent topics in C. Sanz's work include Video Coding and Compression Technologies (32 papers), Advanced Data Compression Techniques (23 papers) and Remote-Sensing Image Classification (20 papers). C. Sanz is often cited by papers focused on Video Coding and Compression Technologies (32 papers), Advanced Data Compression Techniques (23 papers) and Remote-Sensing Image Classification (20 papers). C. Sanz collaborates with scholars based in Spain, France and Italy. C. Sanz's co-authors include Eduardo Juárez, M.J. Garrido, Fernando Pescador, Miguel Chavarrías, Daniel Madroñal, Rubén Salvador, Raquel Lazcano, Himar Fabelo, Samuel Ortega and Gustavo M. Callicó and has published in prestigious journals such as IEEE Access, Sensors and Remote Sensing.

In The Last Decade

C. Sanz

72 papers receiving 572 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
C. Sanz Spain 13 303 241 99 93 90 77 596
Eduardo Juárez Spain 12 237 0.8× 165 0.7× 102 1.0× 96 1.0× 97 1.1× 79 551
Rubén Salvador Spain 12 103 0.3× 37 0.2× 55 0.6× 81 0.9× 73 0.8× 45 454
S. Ramachandran India 15 454 1.5× 191 0.8× 16 0.2× 86 0.9× 18 0.2× 79 683
Jinjun Wang China 14 735 2.4× 160 0.7× 58 0.6× 158 1.7× 2 0.0× 34 844
Philipp Benz South Korea 11 252 0.8× 44 0.2× 22 0.2× 23 0.2× 12 0.1× 12 447
Xiaogang Du China 13 230 0.8× 6 0.0× 67 0.7× 98 1.1× 91 1.0× 35 497
Gautier Izacard France 4 244 0.8× 22 0.1× 64 0.6× 44 0.5× 5 0.1× 6 497
D.R. Bull United Kingdom 13 484 1.6× 178 0.7× 19 0.2× 264 2.8× 4 0.0× 65 697
Sara Sabour United States 5 269 0.9× 30 0.1× 40 0.4× 27 0.3× 5 0.1× 5 462

Countries citing papers authored by C. Sanz

Since Specialization
Citations

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

Fields of papers citing papers by C. Sanz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of C. Sanz

This figure shows the co-authorship network connecting the top 25 collaborators of C. Sanz. A scholar is included among the top collaborators of C. Sanz 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 C. Sanz. C. Sanz 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.
Castaño‐León, Ana M., Elena Salvador Álvarez, Ana Ramos, et al.. (2025). Comprehensive predictive modeling in subarachnoid hemorrhage: integrating radiomics and clinical variables. Neurosurgical Review. 48(1). 528–528.
2.
León, Raquel, Himar Fabelo, Samuel Ortega, et al.. (2025). Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification: Towards robust algorithm development. Acceda (Universidad de Las Palmas de Gran Canaria). 7. 100183–100183. 2 indexed citations
3.
Chavarrías, Miguel, et al.. (2025). Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications. International Journal of Computer Assisted Radiology and Surgery. 20(8). 1759–1769.
4.
Chavarrías, Miguel, et al.. (2024). HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications. International Journal of Computer Assisted Radiology and Surgery. 19(7). 1367–1374. 5 indexed citations
5.
Lagares, Alfonso, et al.. (2024). Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification. Journal of Biomedical Optics. 29(9). 93510–93510. 2 indexed citations
6.
Pérez‐Núñez, Ángel, Luis Jiménez‐Roldán, Alfonso Lagares, et al.. (2023). Sparse to Dense Ground Truth Pre-Processing in Hyperspectral Imaging for In-Vivo Brain Tumour Detection. 15. 272–277. 2 indexed citations
7.
Chavarrías, Miguel, et al.. (2022). Hyperparameter Optimization for Brain Tumor Classification with Hyperspectral Images. 835–842. 6 indexed citations
8.
Jiménez‐Roldán, Luis, Ángel Pérez‐Núñez, Alfonso Lagares, et al.. (2022). Exploration of Realtime Brain tumor classification from Hyperspectral Images in Heterogeneous Embedded MPSoC. 1–6. 2 indexed citations
9.
Jiménez‐Roldán, Luis, et al.. (2021). Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. Sensors. 21(11). 3827–3827. 57 indexed citations
10.
Florimbi, Giordana, Himar Fabelo, Emanuele Torti, et al.. (2018). Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images. Sensors. 18(7). 2314–2314. 30 indexed citations
11.
Pescador, Fernando, et al.. (2017). Real-time HEVC decoding with OpenHEVC and OpenMP. 370–371. 2 indexed citations
12.
Madroñal, Daniel, Raquel Lazcano, Himar Fabelo, et al.. (2016). Hyperspectral image classification using a parallel implementation of the linear SVM on a Massively Parallel Processor Array (MPPA) platform. Acceda (Universidad de Las Palmas de Gran Canaria). 3. 154–160. 3 indexed citations
13.
Lazcano, Raquel, Daniel Madroñal, Karol Desnos, et al.. (2016). Parallelism exploitation of a PCA algorithm for hyperspectral images using RVC-CAL. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10007. 100070H–100070H. 2 indexed citations
14.
15.
Juárez, Eduardo, et al.. (2013). System-level PMC-driven energy estimation models in RVC-CAL video codec specifications. 55–62. 2 indexed citations
16.
Juárez, Eduardo, et al.. (2013). A PMC-driven methodology for energy estimation in RVC-CAL video codec specifications. Signal Processing Image Communication. 28(10). 1303–1314. 11 indexed citations
17.
Juárez, Eduardo, et al.. (2011). A DSP based H.264/SVCdecoder for a multimedia terminal. IEEE Transactions on Consumer Electronics. 57(2). 705–712. 5 indexed citations
18.
Sanz, C., et al.. (2008). A DSP Based H.264 Dec oder for a Multi-Format IP Set-Top Box. 1–2. 1 indexed citations
19.
Pescador, Fernando, et al.. (2007). A real-time H.264 MP decoder based on a DM642 DSP. 1248–1251. 4 indexed citations
20.
Arriaga, J. & C. Sanz. (2002). The three-year engineering curriculum: A difficult balance. 297–300. 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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