Inge Gavăt

472 citations
63 papers · 378 · h-index 9

Impact in

Papers in

Inge Gavăt

56 papers receiving 358 citations

Peers

Inge Gavăt
Comparison fields: 5 of 62
  • Media Technology 155
  • Signal Processing 114
  • Computer Vision and Pattern Recognition 157
  • Artificial Intelligence 143
  • Atmospheric Science 52
Replace Sylvie Philipp‐Foliguet with:
Sylvie Philipp‐Foliguet France
Konstantina Fotiadou Greece
Christopher F. Barnes United States
Zhong Chen China
John A. Saghri United States
Nicholas H. Younan United States
M. V. Shapiro United States
Mohamed Dahmane Canada
Chintan A. Shah United States
Xuee Rong China
Inge Gavăt relative to Sylvie Philipp‐Foliguet France Sylvie Philipp‐Foliguet's profile →
Citations per field
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Sylvie Philipp‐Foliguet · 1×
Citations per year

Countries citing papers authored by Inge Gavăt

Since Specialization
Citations

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

Fields of papers citing papers by Inge Gavăt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 7 scholars most cited alongside Inge Gavăt, 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 Inge Gavăt Line = papers co-authored together Inge Gavăt links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 63 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201264
2 201153
3 200926
4 201223
5
Deep learning in very high resolution remote sensing image information mining communication concept
201222
6 200617
7 200214
8 199912
9 200812
10 20099
11 20159
12 20029
13 20099
14 20056
15 20076
16 20085
17 20054
18 20024
19 20194
20 20104

About Inge Gavăt

Inge Gavăt is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Media Technology, Artificial Intelligence and Atmospheric Science, having authored 63 papers that have together received 378 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (28 papers), Speech and Audio Processing (25 papers), Remote-Sensing Image Classification (13 papers), Advanced Image and Video Retrieval Techniques (11 papers), Image Retrieval and Classification Techniques (10 papers), Music and Audio Processing (8 papers), Neural Networks and Applications (7 papers) and Speech and dialogue systems (6 papers). The work is most often cited by research in Media Technology (155 citations), Signal Processing (114 citations), Computer Vision and Pattern Recognition (157 citations), Artificial Intelligence (143 citations) and Atmospheric Science (52 citations). Inge Gavăt has collaborated with scholars based in Romania, Germany and Pakistan. Frequent co-authors include Mihai Datcu, Corneliu Octavian Dumitru, Oana G. Cula, Valentin Enescu, George Dan Suciu, Markus Canazei and Klaus Becker. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, International Journal of Speech Technology, IEEE Geoscience and Remote Sensing Letters, Lecture notes in computer science and Advances in Science Technology and Engineering Systems Journal.

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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