Minerva M. Yeung

4.1k citations
48 papers · 2.6k indexed · h-index 25

Minerva M. Yeung

48 papers receiving 2.3k citations

Peers

Minerva M. Yeung
Comparison fields: 5 of 79
  • Computer Vision and Pattern Recognition 2.4k
  • Signal Processing 672
  • Sociology and Political Science 287
  • Information Systems 179
  • Computer Networks and Communications 161
Replace Frank Hartung with:
Frank Hartung Germany
Fabien A. P. Petitcolas United Kingdom
Tung-Shou Chen Taiwan
R.L. Lagendijk Netherlands
Ali C. Begen Türkiye
Xiaoqing Zhu United States
P. Eades Australia
Isao Echizen Japan
Jack Brassil United States
Justin Talbot United States
Minerva M. Yeung relative to Frank Hartung Germany Frank Hartung's profile →
Citations per field
00.5×3.1×
Frank Hartung · 1×
Citations per year

Countries citing papers authored by Minerva M. Yeung

Since Specialization
Citations

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

Fields of papers citing papers by Minerva M. Yeung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Minerva M. Yeung. 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 Minerva M. Yeung. The network helps show where Minerva M. Yeung may publish in the future.

Co-authorship network of co-authors of Minerva M. Yeung

This figure shows the co-authorship network connecting the top 25 collaborators of Minerva M. Yeung. A scholar is included among the top collaborators of Minerva M. Yeung 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 Minerva M. Yeung. Minerva M. Yeung 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
#WorkIndexed citations
1
Storage and Retrieval Methods and Applications for Multimedia 2004
30
2 26
3 70
4 8
5 21
6 17
7 9
8 2
9
Content Protection and Delivery System for the Internet.
1
10
Storage and Retrieval for Media Databases 2000
9
11 29
12
Storage and Retrieval for Image and Video Databases VII
6
13 47
14 161
15 59
16 6
17 63
18 24
19
Analysis, modeling and representation of digital video
3
20 123

About Minerva M. Yeung

Minerva M. Yeung is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computer Graphics and Computer-Aided Design, having authored 48 papers that have together received 2.6k indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (22 papers), Video Analysis and Summarization (16 papers) and Chaos-based Image/Signal Encryption (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Signal Processing (672 citations) and Computer Graphics and Computer-Aided Design (123 citations). Minerva M. Yeung has collaborated with scholars based in United States. Frequent co-authors include Boon-Lock Yeo, Bede Liu, F. Mintzer, Scott Craver, Nasir Memon, Kannan Ramchandran, I. Kozintsev, D.G. Sachs, Gordon W. Braudaway and Wayne Wolf. Their work appears in journals such as Communications of the ACM, IEEE Journal on Selected Areas in Communications and IEEE Transactions on Circuits and Systems for Video Technology.

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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