Daniel Acevedo

560 citations
30 papers · 278 · h-index 10

Impact in

Papers in

    • Image and Signal Denoising Methods 7
    • Advanced Data Compression Techniques 6
    • Advanced Image and Video Retrieval Techniques 5
    • Face recognition and analysis 4
    • Face and Expression Recognition 3
    • Image Retrieval and Classification Techniques 3
    • Interactive and Immersive Displays 3

Daniel Acevedo

26 papers receiving 269 citations

Peers

Daniel Acevedo
Comparison fields: 5 of 62
  • Computer Graphics and Computer-Aided Design 52
  • Human-Computer Interaction 79
  • Computer Vision and Pattern Recognition 199
  • Geology 23
  • Media Technology 30
Replace Ronell Sicat with:
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Citations per field
00.5×3.3×
Ronell Sicat · 1×
Citations per year

Countries citing papers authored by Daniel Acevedo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Acevedo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Daniel Acevedo, 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 Daniel Acevedo Line = papers co-authored together Daniel Acevedo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200841
2 202229
3 200127
4 200125
5 201424
6 200822
7 200614
8 201713
9 200312
10 20169
11 20217
12 20037
13 20096
14 20126
15 20106
16 20065
17 20054
18 20053
19 20103
20 20233

About Daniel Acevedo

Daniel Acevedo is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Media Technology, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 30 papers that have together received 278 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (7 papers), Advanced Data Compression Techniques (6 papers), Advanced Image and Video Retrieval Techniques (5 papers), Face recognition and analysis (4 papers), Computer Graphics and Visualization Techniques (4 papers), Face and Expression Recognition (3 papers), Image Retrieval and Classification Techniques (3 papers) and Interactive and Immersive Displays (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (52 citations), Human-Computer Interaction (79 citations), Computer Vision and Pattern Recognition (199 citations), Geology (23 citations) and Media Technology (30 citations). Daniel Acevedo has collaborated with scholars based in Argentina, United States and Spain. Frequent co-authors include David H. Laidlaw, Martha Sharp Joukowsky, Daniel F. Keefe, Cullen D. Jackson, Marta Mejail, Sharon M. Swartz, Esther de Ves, Sarath Chandra Janga, Pablo Negri and Phuc Nguyen. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Methods, IEEE Geoscience and Remote Sensing Letters, Pattern Recognition Letters and Experimental Biology and Medicine.

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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