Graça Bressan

1.0k citations
64 papers · 685 · h-index 14

Impact in

Papers in

Graça Bressan

57 papers receiving 628 citations

Peers

Graça Bressan
Comparison fields: 5 of 73
  • Signal Processing 164
  • Computer Vision and Pattern Recognition 282
  • Computer Science Applications 46
  • Information Systems 155
  • Artificial Intelligence 178
Replace Kuan‐Ta Chen with:
Kuan‐Ta Chen Taiwan
Jiuxin Cao China
Nicholas Race United Kingdom
Oliver Hohlfeld Germany
Bruno Gardlo Austria
Dilip Joseph United States
Lucjan Janowski Poland
Srinivas Krishnan United States
Mohammad Akbari Iran
Graça Bressan relative to Kuan‐Ta Chen Taiwan Kuan‐Ta Chen's profile →
Citations per field
00.5×1.7×
Kuan‐Ta Chen · 1×
Citations per year

Countries citing papers authored by Graça Bressan

Since Specialization
Citations

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

Fields of papers citing papers by Graça Bressan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Graça Bressan, 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 Graça Bressan Line = papers co-authored together Graça Bressan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201583
2 201770
3 201250
4 201650
5 201439
6 201439
7 201325
8 201324
9 200724
10 201620
11 201618
12 200817
13 201515
14 201014
15 201212
16 201212
17 201212
18 202311
19 201611
20 20028

About Graça Bressan

Graça Bressan is a scholar working on Computer Vision and Pattern Recognition, Sociology and Political Science, Computer Networks and Communications, Information Systems and Artificial Intelligence, having authored 64 papers that have together received 685 indexed citations. Recurring topics across this work include Multimedia Communication and Technology (16 papers), Image and Video Quality Assessment (15 papers), Video Analysis and Summarization (7 papers), Video Coding and Compression Technologies (6 papers), Peer-to-Peer Network Technologies (6 papers), Open Education and E-Learning (5 papers), IoT and Edge/Fog Computing (5 papers) and Sentiment Analysis and Opinion Mining (5 papers). The work is most often cited by research in Signal Processing (164 citations), Computer Vision and Pattern Recognition (282 citations), Computer Science Applications (46 citations), Information Systems (155 citations) and Artificial Intelligence (178 citations). Graça Bressan has collaborated with scholars based in Brazil, United States and Italy. Frequent co-authors include Demóstenes Zegarra Rodríguez, Renata Lopes Rosa, Júlia Issy Abrahão, Renata Lopes Rosa, Luciana Zaina, Zhou Wang, Wilson Vicente Ruggiero, Eduardo Costa, José F. Rodrigues and João Moreno. Their work appears in journals such as IEEE Transactions on Consumer Electronics, Sensors, EURASIP Journal on Wireless Communications and Networking, IEEE Transactions on Broadcasting and Computer Communications.

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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