Dan Lelescu

565 citations
16 papers · 382 · h-index 8

Impact in

Papers in

Dan Lelescu

15 papers receiving 351 citations

Peers

Dan Lelescu
Comparison fields: 5 of 47
  • Computer Vision and Pattern Recognition 225
  • Media Technology 84
  • Acoustics and Ultrasonics 7
  • Transportation 47
  • Computer Networks and Communications 113
Replace Jaeyeon Kang with:
Jaeyeon Kang United States
Maurizio Lucenteforte Italy
Gwangsoon Lee South Korea
Eric Debes United States
Sihui Luo China
Min-Cheol Hong South Korea
Thomas Maugey France
N.K. Shankaranarayanan United States
C.J. van den Branden Lambrecht Switzerland
Branka Zovko-Cihlar Croatia
Dan Lelescu relative to Jaeyeon Kang United States Jaeyeon Kang's profile →
Citations per field
00.5×3.6×
Jaeyeon Kang · 1×
Citations per year

Countries citing papers authored by Dan Lelescu

Since Specialization
Citations

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

Fields of papers citing papers by Dan Lelescu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 12 scholars most cited alongside Dan Lelescu, 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 Dan Lelescu Line = papers co-authored together Dan Lelescu links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2013157
2 200365
3 200548
4 200640
5 200419
6 200017
7 200410
8 19988
9 20065
10 20025
11 19983
12 20032
13 20081
14 20021
15 20161
16 20060

About Dan Lelescu

Dan Lelescu is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications, Signal Processing, Media Technology and Electrical and Electronic Engineering, having authored 16 papers that have together received 382 indexed citations. Recurring topics across this work include Video Analysis and Summarization (7 papers), Wireless Networks and Protocols (5 papers), Advanced Vision and Imaging (5 papers), Advanced Image and Video Retrieval Techniques (5 papers), Opportunistic and Delay-Tolerant Networks (4 papers), Mobile Ad Hoc Networks (4 papers), Image Retrieval and Classification Techniques (3 papers) and Video Coding and Compression Technologies (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (225 citations), Media Technology (84 citations), Acoustics and Ultrasonics (7 citations), Transportation (47 citations) and Computer Networks and Communications (113 citations). Dan Lelescu has collaborated with scholars based in United States and Belgium. Frequent co-authors include Dan Schonfeld, Ravi Jain, Shree K. Nayar, Andrew McMahon, Kartik Venkataraman, Jacques Duparré, Priyam Chatterjee, Ulaş C. Kozat, Frank Bossen and Xiaoning He. Their work appears in journals such as EURASIP Journal on Wireless Communications and Networking, ACM Transactions on Graphics, Wireless Networks, Graphical Models and Journal of Visual Communication and Image Representation.

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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