Moises Díaz

2.5k citations
101 papers · 1.9k · h-index 22

Impact in

Papers in

Moises Díaz

94 papers receiving 1.8k citations

Peers

Moises Díaz
Comparison fields: 5 of 114
  • Computer Vision and Pattern Recognition 1.4k
  • Media Technology 402
  • Human-Computer Interaction 240
  • Signal Processing 218
  • Artificial Intelligence 501
Replace Xi Zhou with:
Xi Zhou China
John See Malaysia
Tim K. Marks United States
Modesto Castrillón-Santana Spain
Alex Zelinsky Australia
Liyuan Li Singapore
Javier Lorenzo-Navarro Spain
Kang Park South Korea
Hazım Kemal Ekenel Türkiye
Xiaojiang Peng China
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Citations per field
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Xi Zhou · 1×
Citations per year

Countries citing papers authored by Moises Díaz

Since Specialization
Citations

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

Fields of papers citing papers by Moises Díaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Moises Díaz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Moises Díaz Line = papers co-authored together Moises Díaz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 101 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019178
2 2016106
3 201598
4 201497
5 201493
6 201987
7 202085
8 202082
9 201670
10 201366
11 201266
12 201652
13 201548
14 201548
15 201845
16 201840
17 201534
18 201533
19 201833
20 201528

About Moises Díaz

Moises Díaz is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction, Control and Systems Engineering and Signal Processing, having authored 101 papers that have together received 1.9k indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (59 papers), Image Processing and 3D Reconstruction (24 papers), Natural Language Processing Techniques (22 papers), Hand Gesture Recognition Systems (17 papers), Image Retrieval and Classification Techniques (11 papers), Vehicle License Plate Recognition (7 papers), Biometric Identification and Security (5 papers) and Robot Manipulation and Learning (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Media Technology (402 citations), Human-Computer Interaction (240 citations), Signal Processing (218 citations) and Artificial Intelligence (501 citations). Moises Díaz has collaborated with scholars based in Spain, Italy and Canada. Frequent co-authors include Miguel A. Ferrer, Aythami Morales, Réjean Plamondon, Giuseppe Pirlo, Donato Impedovo, Pietro Cerri, Cristina Carmona-Duarte, Andreas Fischer, Gennaro Vessio and Imran Siddiqi. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition Letters, Cognitive Computation, Expert Systems with Applications and Pattern Recognition.

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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