Sergio Schuler
Impact in
- Media Technology top 5%
- Advanced Image Fusion Techniques
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- Image and Signal Denoising Methods
- Medical Image Segmentation Techniques
- Image Enhancement Techniques
- Image Retrieval and Classification Techniques
- Advanced Data Compression Techniques
Papers in
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- Image and Signal Denoising Methods 4
- Medical Image Segmentation Techniques 2
- Image Retrieval and Classification Techniques 2
- Image Processing and 3D Reconstruction 1
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- Advanced Image Fusion Techniques 3
- Co-authors
- Andrew F. Laine (7 shared papers)Jian Fan (2 shared papers)W Huda (1 shared paper)Walter Huda (1 shared paper)Jingtao Fan (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (1 paper)Machine Vision and Applications (1 paper)Columbia Academic Commons (Columbia University) (2 papers)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2 papers)
- Partner nations
- United States
In The Last Decade
Sergio Schuler
7 papers receiving 269 citations
Peers
Comparison fields: 5 of 46
- Media Technology 130
- Computer Vision and Pattern Recognition 253
- Artificial Intelligence 112
- Radiology, Nuclear Medicine and Imaging 56
- Biophysics 7
Countries citing papers authored by Sergio Schuler
This map shows the geographic impact of Sergio Schuler'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 Sergio Schuler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergio Schuler more than expected).
Fields of papers citing papers by Sergio Schuler
This network shows the impact of papers produced by Sergio Schuler. 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 Sergio Schuler. The network helps show where Sergio Schuler may publish in the future.
Co-authors
The 5 scholars most cited alongside Sergio Schuler, 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 | 1994 | 255 | |
| 2 | 1993 | 22 | |
| 3 | 1993 | 9 | |
| 4 | Hexagonal Wavelet Representations for Recognizing Complex Annotations | 1993 | 7 |
| 5 | 1994 | 4 | |
| 6 | 1998 | 3 | |
| 7 | 2002 | 2 |
About Sergio Schuler
Sergio Schuler is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Artificial Intelligence, Biomedical Engineering and Cardiology and Cardiovascular Medicine, having authored 7 papers that have together received 302 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (4 papers), Advanced Image Fusion Techniques (3 papers), Photoacoustic and Ultrasonic Imaging (2 papers), Medical Image Segmentation Techniques (2 papers), Image Retrieval and Classification Techniques (2 papers), Image Processing and 3D Reconstruction (1 paper), Time Series Analysis and Forecasting (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Media Technology (130 citations), Computer Vision and Pattern Recognition (253 citations), Artificial Intelligence (112 citations), Radiology, Nuclear Medicine and Imaging (56 citations) and Biophysics (7 citations). Sergio Schuler has collaborated with scholars based in United States. Frequent co-authors include Andrew F. Laine, Jian Fan, W Huda, Walter Huda and Jingtao Fan. Their work appears in journals such as IEEE Transactions on Medical Imaging, Machine Vision and Applications, Columbia Academic Commons (Columbia University) and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.