Ignacio Ramírez

1.2k total citations · 1 hit paper
18 papers, 791 citations indexed

About

Ignacio Ramírez is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Signal Processing. According to data from OpenAlex, Ignacio Ramírez has authored 18 papers receiving a total of 791 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 10 papers in Computational Mechanics and 6 papers in Signal Processing. Recurrent topics in Ignacio Ramírez's work include Image and Signal Denoising Methods (9 papers), Sparse and Compressive Sensing Techniques (9 papers) and Blind Source Separation Techniques (6 papers). Ignacio Ramírez is often cited by papers focused on Image and Signal Denoising Methods (9 papers), Sparse and Compressive Sensing Techniques (9 papers) and Blind Source Separation Techniques (6 papers). Ignacio Ramírez collaborates with scholars based in Uruguay, United States and Israel. Ignacio Ramírez's co-authors include Guillermo Sapiro, Pablo Sprechmann, G. Seroussi, Federico Lecumberry, M.J. Weinberger, Erik Ordentlich, Gaetano Motta, Yonina C. Eldar, Federico Favaro and Carlo M. Biancardi and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing and IEEE Journal of Selected Topics in Signal Processing.

In The Last Decade

Ignacio Ramírez

17 papers receiving 773 citations

Hit Papers

align trajectories

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.

Classification and clustering via dictionary learning with structured incoherence and shared features

2010 532 citations Ignacio Ramírez, Pablo Sprechmann et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ignacio Ramírez Uruguay	10	532	338	176	162	152	18	791
Francis Bach France	2	514 1.0×	314 0.9×	140 0.8×	134 0.8×	118 0.8×	2	717
Hossein Mobahi United States	12	837 1.6×	242 0.7×	231 1.3×	103 0.6×	198 1.3×	26	1.1k
Debing Zhang China	10	661 1.2×	418 1.2×	119 0.7×	161 1.0×	231 1.5×	26	1.0k
Karl Skretting Norway	9	369 0.7×	341 1.0×	74 0.4×	79 0.5×	196 1.3×	28	653
Yao Hu China	9	504 0.9×	445 1.3×	76 0.4×	90 0.6×	159 1.0×	15	799
Xiangyu Chang China	3	297 0.6×	495 1.5×	126 0.7×	70 0.4×	109 0.7×	4	787
Avinash Ravichandran United States	11	831 1.6×	142 0.4×	465 2.6×	123 0.8×	107 0.7×	18	1.1k
Zhonghua Liu China	16	551 1.0×	148 0.4×	248 1.4×	188 1.2×	82 0.5×	66	821
Andrew Wagner United States	7	739 1.4×	312 0.9×	92 0.5×	105 0.6×	210 1.4×	14	887

Countries citing papers authored by Ignacio Ramírez

Since Specialization

Citations

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

Fields of papers citing papers by Ignacio Ramírez

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ignacio Ramírez

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

All Works

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18 of 18 papers shown

Ramírez, Ignacio, et al.. (2021). Common motor patterns of asymmetrical and symmetrical bipedal gaits. PeerJ. 9. e11970–e11970. 11 indexed citations

Ramírez, Ignacio, et al.. (2021). Teaching a Robot the optimal operation of an Electrical Energy System with high integration of renewable energies. 52. 364–367.

Ramírez, Ignacio, et al.. (2021). Image Inpainting using Patch Consensus and DCT Priors. Image Processing On Line. 11. 1–17. 1 indexed citations

Favaro, Federico, et al.. (2018). Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless and Near-Lossless Compression. IEEE Transactions on Biomedical Circuits and Systems. 12(1). 231–241. 25 indexed citations

Ramírez, Ignacio. (2018). Binary Matrix Factorization via Dictionary Learning. IEEE Journal of Selected Topics in Signal Processing. 12(6). 1253–1262. 1 indexed citations

Favaro, Federico, et al.. (2016). Wearable EEG via lossless compression. PubMed. 2016. 1995–1998. 9 indexed citations

Lecumberry, Federico, et al.. (2014). Low-Complexity, Multi-Channel, Lossless And Near-Lossless Eeg Compression. Zenodo (CERN European Organization for Nuclear Research). 2040–2044. 7 indexed citations

Ramírez, Ignacio & Guillermo Sapiro. (2012). Universal Regularizers for Robust Sparse Coding and Modeling. IEEE Transactions on Image Processing. 21(9). 3850–3864. 24 indexed citations

Ramírez, Ignacio & Guillermo Sapiro. (2012). An MDL Framework for Sparse Coding and Dictionary Learning. IEEE Transactions on Signal Processing. 60(6). 2913–2927. 37 indexed citations

10.

Ramírez, Ignacio & Guillermo Sapiro. (2012). LOw-rank data modeling via the minimum description length principle. 2165–2168. 6 indexed citations

11.

Sprechmann, Pablo, et al.. (2011). Collaborative sources identification in mixed signals via hierarchical sparse modeling. 5816–5819. 4 indexed citations

12.

Ramírez, Ignacio & Guillermo Sapiro. (2011). Sparse coding and dictionary learning based on the MDL principle. 32. 2160–2163. 4 indexed citations

13.

Motta, Gaetano, Erik Ordentlich, Ignacio Ramírez, G. Seroussi, & M.J. Weinberger. (2010). The iDUDE Framework for Grayscale Image Denoising. IEEE Transactions on Image Processing. 20(1). 1–21. 46 indexed citations

14.

Sprechmann, Pablo, Ignacio Ramírez, Guillermo Sapiro, & Yonina C. Eldar. (2010). Collaborative hierarchical sparse modeling. 1–6. 30 indexed citations

15.

Ramírez, Ignacio, Pablo Sprechmann, & Guillermo Sapiro. (2010). Classification and clustering via dictionary learning with structured incoherence and shared features. 3501–3508. 532 indexed citations breakdown →

16.

Ramírez, Ignacio, Federico Lecumberry, & Guillermo Sapiro. (2009). Universal priors for sparse modeling. 197–200. 22 indexed citations

17.

Ramírez, Ignacio, Federico Lecumberry, & Guillermo Sapiro. (2009). Sparse Modeling with Universal Priors and Learned Incoherent Dictionaries. University of Minnesota Digital Conservancy (University of Minnesota). 15 indexed citations

18.

Motta, Gaetano, Erik Ordentlich, Ignacio Ramírez, G. Seroussi, & M.J. Weinberger. (2005). The DUDE framework for continuous tone image denoising. III–345. 17 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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