Matei Stroila

795 citations
17 papers · 666 · h-index 9

Impact in

Papers in

Matei Stroila

15 papers receiving 635 citations

Peers

Matei Stroila
Comparison fields: 5 of 58
  • Neurology 205
  • Pathology and Forensic Medicine 166
  • Geology 43
  • Genetics 59
  • Epidemiology 164
Replace Antonio Guiducci with:
Antonio Guiducci Italy
Taichi Kin Japan
Chung‐Jung Lin Taiwan
Michael C. Huang United States
Wen C. Yang United States
Orlando Díaz United States
John E. Crossman United Kingdom
Susanna Bacigaluppi Italy
Marguerite Müller Germany
Gregor Richter Germany
Matei Stroila relative to Antonio Guiducci Italy Antonio Guiducci's profile →
Citations per field
00.5×2×4×6×7.5×
Antonio Guiducci · 1×
Citations per year

Countries citing papers authored by Matei Stroila

Since Specialization
Citations

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

Fields of papers citing papers by Matei Stroila

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 20 scholars most cited alongside Matei Stroila, 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 Matei Stroila Line = papers co-authored together Matei Stroila links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 2006189
2 2005172
3 200574
4 200574
5 200963
6 200529
7 200526
8 200715
9 20169
10 20114
11 20164
12 20092
13 20142
14 20122
15 20111
16 20190
17 20140

About Matei Stroila

Matei Stroila is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Computer Graphics and Computer-Aided Design, Transportation and Automotive Engineering, having authored 17 papers that have together received 666 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Computer Graphics and Visualization Techniques (2 papers), Vascular Malformations Diagnosis and Treatment (2 papers), Robotic Path Planning Algorithms (2 papers), Remote Sensing and LiDAR Applications (2 papers), Brain Metastases and Treatment (2 papers) and Video Surveillance and Tracking Methods (2 papers). The work is most often cited by research in Neurology (205 citations), Pathology and Forensic Medicine (166 citations), Geology (43 citations), Genetics (59 citations) and Epidemiology (164 citations). Matei Stroila has collaborated with scholars based in United States, France and Netherlands. Frequent co-authors include Jason P. Sheehan, Ladislau E. Steiner, Hung-Chuan Pan, John W. Snell, Melita Steiner, Jeff Bach, Ruisheng Wang, Xin Chen, John C. Hart and Elmar Eisemann. Their work appears in journals such as Journal of neurosurgery, IEEE Transactions on Visualization and Computer Graphics, Lecture notes in computer science and TU/e Research Portal.

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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