Eva Cetinić

1.0k citations
11 papers · 534 · 1 hit paper · h-index 8

Impact in

Papers in

Eva Cetinić

10 papers receiving 514 citations

Eva Cetinić's Hit Papers

Understanding and Creating Art with AI: Review and Outlook 2022 · 250 citations
2500+1+2Years since publication50100150200250

Peers

Eva Cetinić
Comparison fields: 5 of 105
  • Computer Graphics and Computer-Aided Design 70
  • Computer Vision and Pattern Recognition 239
  • Cognitive Neuroscience 202
  • Health Informatics 14
  • Human-Computer Interaction 53
Replace William Latham with:
William Latham United Kingdom
Stavroula Ntoa Greece
Nicola Orio Italy
Casey Reas United States
John Maeda United States
Manish Singh India
Ming Cheung Hong Kong
Leigh McLoughlin United Kingdom
John Lasseter
Maxwell Forbes United States
Eva Cetinić relative to William Latham United Kingdom William Latham's profile →
Citations per field
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William Latham · 1×
Citations per year

Countries citing papers authored by Eva Cetinić

Since Specialization
Citations

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

Fields of papers citing papers by Eva Cetinić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 8 scholars most cited alongside Eva Cetinić, 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 Eva Cetinić Line = papers co-authored together Eva Cetinić links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
Understanding and Creating Art with AI: Review and Outlook
Hit paper breakdown →
2022250
2 2018156
3 201944
4 201927
5 202116
6
Automated painter recognition based on image feature extraction
201315
7 201612
8 202310
9 20192
10 20182
11 20250

About Eva Cetinić

Eva Cetinić is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience, Conservation, Computer Networks and Communications and Control and Systems Engineering, having authored 11 papers that have together received 534 indexed citations. Recurring topics across this work include Aesthetic Perception and Analysis (7 papers), Visual Attention and Saliency Detection (6 papers), Conservation Techniques and Studies (4 papers), Hand Gesture Recognition Systems (1 paper), Digital Media and Visual Art (1 paper), Multimodal Machine Learning Applications (1 paper), Image Enhancement Techniques (1 paper) and Cultural Heritage Materials Analysis (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (70 citations), Computer Vision and Pattern Recognition (239 citations), Cognitive Neuroscience (202 citations), Health Informatics (14 citations) and Human-Computer Interaction (53 citations). Eva Cetinić has collaborated with scholars based in Croatia, United Kingdom and Switzerland. Frequent co-authors include James She, Sonja Grgić, Tomislav Lipić, Davor Davidović, Davide Salomoni, Germán Moltó, Miguel Caballer and Giacinto Donvito. Their work appears in journals such as ACM Transactions on Multimedia Computing Communications and Applications, IEEE Access, Expert Systems with Applications, Pattern Recognition Letters and Journal of Imaging.

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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