Cedric Scheerlinck
Impact in
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- Advanced Neural Network Applications
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Image and Signal Denoising Methods
Papers in
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- Advanced Memory and Neural Computing 4
- CCD and CMOS Imaging Sensors 2
- Ferroelectric and Negative Capacitance Devices 1
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- Atomic and Subatomic Physics Research 2
- Co-authors
- Robert Mahony (3 shared papers)Davide Scaramuzza (2 shared papers)Nick Barnes (2 shared papers)Henri Rebecq (1 shared paper)Daniel Gehrig (1 shared paper)Lindsay Kleeman (1 shared paper)Tom Drummond (1 shared paper)Miaomiao Liu (1 shared paper)
- Journals
- Journal of Jewish Studies (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)arXiv (Cornell University) (1 paper)Open MIND (1 paper)
- Partner nations
- AustraliaSwitzerland
In The Last Decade
Cedric Scheerlinck
5 papers receiving 142 citations
Peers
Comparison fields: 5 of 37
- Acoustics and Ultrasonics 4
- Computer Vision and Pattern Recognition 60
- Instrumentation 6
- Cognitive Neuroscience 31
- Electrical and Electronic Engineering 89
Countries citing papers authored by Cedric Scheerlinck
This map shows the geographic impact of Cedric Scheerlinck'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 Cedric Scheerlinck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cedric Scheerlinck more than expected).
Fields of papers citing papers by Cedric Scheerlinck
This network shows the impact of papers produced by Cedric Scheerlinck. 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 Cedric Scheerlinck. The network helps show where Cedric Scheerlinck may publish in the future.
Co-authors
The 12 scholars most cited alongside Cedric Scheerlinck, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 139 | |
| 2 | 2023 | 6 | |
| 3 | How to Train Your Event Camera Neural Network | 2020 | 5 |
| 4 | Bringing Blurry Alive at High Frame-Rate with an Event Camera. | 2019 | 1 |
| 5 | 2020 | 1 |
About Cedric Scheerlinck
Cedric Scheerlinck is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Information Systems and Management, Cognitive Neuroscience and Radiology, Nuclear Medicine and Imaging, having authored 5 papers that have together received 152 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (4 papers), Atomic and Subatomic Physics Research (2 papers), CCD and CMOS Imaging Sensors (2 papers), Scientific Computing and Data Management (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Electronic and Structural Properties of Oxides (1 paper), Neural dynamics and brain function (1 paper) and Advanced MRI Techniques and Applications (1 paper). The work is most often cited by research in Acoustics and Ultrasonics (4 citations), Computer Vision and Pattern Recognition (60 citations), Instrumentation (6 citations), Cognitive Neuroscience (31 citations) and Electrical and Electronic Engineering (89 citations). Cedric Scheerlinck has collaborated with scholars based in Australia and Switzerland. Frequent co-authors include Robert Mahony, Davide Scaramuzza, Nick Barnes, Henri Rebecq, Daniel Gehrig, Lindsay Kleeman, Tom Drummond, Miaomiao Liu, Richard Hartley and Xin Yu. Their work appears in journals such as Journal of Jewish Studies, IEEE Transactions on Pattern Analysis and Machine Intelligence, arXiv (Cornell University) and Open MIND.
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.