Amina Chebira

1.4k citations
30 papers · 848 · h-index 13

Impact in

Papers in

Amina Chebira

27 papers receiving 809 citations

Peers

Amina Chebira
Comparison fields: 5 of 79
  • Applied Mathematics 224
  • Biophysics 90
  • Signal Processing 164
  • Computer Vision and Pattern Recognition 288
  • Computational Mechanics 183
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Matthew Fickus United States
Leo Dorst Netherlands
Abderrahim Elmoataz France
Ali Pezeshki United States
C. Si̇nan Güntürk United States
Penghui Wang China
Eckhard Hitzer Japan
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Citations per field
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Citations per year

Countries citing papers authored by Amina Chebira

Since Specialization
Citations

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

Fields of papers citing papers by Amina Chebira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Amina Chebira, 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 Amina Chebira Line = papers co-authored together Amina Chebira 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 2007206
2 2014163
3 200795
4 200793
5 200757
6 201237
7 201133
8 200619
9 201516
10 201115
11 201015
12 200814
13 200713
14 201210
15 201910
16 20049
17 20078
18 20068
19 20077
20 20105

About Amina Chebira

Amina Chebira is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Biophysics, Media Technology and Applied Mathematics, having authored 30 papers that have together received 848 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (7 papers), Image and Signal Denoising Methods (7 papers), Image Processing Techniques and Applications (6 papers), Digital Filter Design and Implementation (6 papers), Mathematical Analysis and Transform Methods (6 papers), Optical measurement and interference techniques (2 papers), Distributed Sensor Networks and Detection Algorithms (2 papers) and Digital Imaging for Blood Diseases (2 papers). The work is most often cited by research in Applied Mathematics (224 citations), Biophysics (90 citations), Signal Processing (164 citations), Computer Vision and Pattern Recognition (288 citations) and Computational Mechanics (183 citations). Amina Chebira has collaborated with scholars based in United States, Switzerland and U.S. Virgin Islands. Frequent co-authors include Jelena Kovačević, Martin Vetterli, Juri Ranieri, Gowri Srinivasa, Robert F. Murphy, C. S. Jackson, Yue M. Lu, Alessandro Vincenzi, David Atienza and Ivan Dokmanić. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Signal Processing Magazine, IEEE Transactions on Information Theory, IEEE Transactions on Computers and BMC Bioinformatics.

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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