Gonzalo Urcid

697 total citations
41 papers, 360 citations indexed

About

Gonzalo Urcid is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Gonzalo Urcid has authored 41 papers receiving a total of 360 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 16 papers in Media Technology. Recurrent topics in Gonzalo Urcid's work include Neural Networks and Applications (15 papers), Remote-Sensing Image Classification (12 papers) and Image Retrieval and Classification Techniques (12 papers). Gonzalo Urcid is often cited by papers focused on Neural Networks and Applications (15 papers), Remote-Sensing Image Classification (12 papers) and Image Retrieval and Classification Techniques (12 papers). Gonzalo Urcid collaborates with scholars based in Mexico and United States. Gonzalo Urcid's co-authors include Gerhard X. Ritter, Mark S. Schmalz, Alfonso Padilla‐Vivanco, Alejandro Cornejo-Rodrı́guez, Carina Toxqui‐Quitl and Carlos A. Reyes-García and has published in prestigious journals such as Pattern Recognition, Information Sciences and Neurocomputing.

In The Last Decade

Gonzalo Urcid

38 papers receiving 340 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gonzalo Urcid Mexico 9 217 147 92 43 42 41 360
Jihun Ham United States 6 189 0.9× 319 2.2× 64 0.7× 68 1.6× 29 0.7× 6 538
Yuchi Huang United States 10 211 1.0× 542 3.7× 82 0.9× 54 1.3× 21 0.5× 16 702
Jianhong Xiang China 14 73 0.3× 184 1.3× 50 0.5× 47 1.1× 42 1.0× 68 521
Donghui Li China 9 59 0.3× 191 1.3× 59 0.6× 29 0.7× 54 1.3× 61 404
Magdi A. Mohamed United States 6 180 0.8× 210 1.4× 82 0.9× 26 0.6× 27 0.6× 20 376
Monica Borda Romania 9 85 0.4× 173 1.2× 34 0.4× 31 0.7× 77 1.8× 64 409
Qibin Zhao Japan 6 137 0.6× 156 1.1× 31 0.3× 68 1.6× 55 1.3× 9 672
Rajib Kumar Jha India 14 155 0.7× 408 2.8× 71 0.8× 25 0.6× 19 0.5× 46 570
Yin‐Ping Zhao China 10 142 0.7× 262 1.8× 59 0.6× 20 0.5× 10 0.2× 28 454
Kiichi Urahama Japan 11 171 0.8× 182 1.2× 47 0.5× 54 1.3× 15 0.4× 138 433

Countries citing papers authored by Gonzalo Urcid

Since Specialization
Citations

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

Fields of papers citing papers by Gonzalo Urcid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gonzalo Urcid

This figure shows the co-authorship network connecting the top 25 collaborators of Gonzalo Urcid. A scholar is included among the top collaborators of Gonzalo Urcid 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 Gonzalo Urcid. Gonzalo Urcid 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.
Urcid, Gonzalo, et al.. (2024). COMPUTATIONAL PERFORMANCE OF HOLE FILLING MORPHOLOGICAL ALGORITHMS FOR BINARY IMAGES. 8(1). 1 indexed citations
2.
Ritter, Gerhard X., et al.. (2020). Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks. Mathematics. 8(9). 1439–1439. 1 indexed citations
3.
Urcid, Gonzalo, et al.. (2015). A dendritic lattice neural network for color image segmentation. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9599. 95992O–95992O. 1 indexed citations
4.
Urcid, Gonzalo, et al.. (2014). Multispectral image restoration of historical documents based on LAAMs and mathematical morphology. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9216. 921604–921604. 4 indexed citations
5.
Urcid, Gonzalo, et al.. (2013). Lattice algebra approach to multispectral analysis of ancient documents. Applied Optics. 52(4). 674–674. 3 indexed citations
6.
Urcid, Gonzalo & Gerhard X. Ritter. (2011). Unsupervised color image segmentation using a lattice algebra clustering technique. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8011. 80117D–80117D. 1 indexed citations
7.
Ritter, Gerhard X. & Gonzalo Urcid. (2011). Perfect recall from noisy input patterns with a dendritic lattice associative memory. 1215. 503–510. 4 indexed citations
8.
Urcid, Gonzalo, et al.. (2010). Multispectral Images Segmentation of Ancient Documents with Lattice Memories. Imaging and Applied Optics Congress. DMD6–DMD6. 2 indexed citations
9.
Urcid, Gonzalo, et al.. (2009). Robust image retrieval from noisy inputs using lattice associative memories. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7245. 724517–724517. 2 indexed citations
10.
Urcid, Gonzalo, et al.. (2009). Endmember search techniques based on lattice auto-associative memories: a case on vegetation discrimination. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7477. 74771D–74771D.
11.
Urcid, Gonzalo, et al.. (2007). Generation of lattice independent vector sets for pattern recognition applications. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6700. 67000C–67000C. 13 indexed citations
12.
Urcid, Gonzalo, et al.. (2007). Enhanced fuzzy autoassociative morphological memory for binary pattern recall. 116–121. 1 indexed citations
13.
Urcid, Gonzalo & Gerhard X. Ritter. (2006). Noise Masking for Pattern Recall Using a Single Lattice Matrix Auto-Associative Memory. 3287. 187–194. 4 indexed citations
14.
Ritter, Gerhard X. & Gonzalo Urcid. (2005). Recent Developments in Lattice Neural Networks. Research in computing science. 14. 1–12. 1 indexed citations
15.
Ritter, Gerhard X., Gonzalo Urcid, & Mark S. Schmalz. (2005). Lattice associative memories that are robust in the presence of noise. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5916. 59160Q–59160Q. 6 indexed citations
16.
Urcid, Gonzalo, et al.. (2004). Segmentación de imágenes de color. Revista Mexicana de Física. 50(6). 579–587. 2 indexed citations
17.
Toxqui‐Quitl, Carina, Alfonso Padilla‐Vivanco, & Gonzalo Urcid. (2004). Multifocus image fusion using the Haar wavelet transform. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5558. 796–796. 4 indexed citations
18.
Padilla‐Vivanco, Alfonso, et al.. (2004). Optical-digital incoherent system for image reconstruction by using Zernike moments. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5237. 290–290. 1 indexed citations
19.
Urcid, Gonzalo & Gerhard X. Ritter. (2003). Kernel Computation in Morphological Associative Memories for Grayscale Image Recollection.. 450–455. 5 indexed citations
20.
Ritter, Gerhard X. & Gonzalo Urcid. (2002). Minimal Representations and Morphological Associative Memories for Pattern Recall. Research in computing science. 1. 27–38. 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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