Rao Muhammad Umer
Impact in
-
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Image and Signal Denoising Methods
-
- Image Processing Techniques and Applications
- Advanced Image Fusion Techniques
Papers in
-
- Advanced Image Processing Techniques 3
- Advanced Vision and Imaging 2
- Image and Signal Denoising Methods 2
- Face recognition and analysis 1
- Digital Imaging for Blood Diseases 1
-
- Privacy-Preserving Technologies in Data 3
- Machine Learning and Algorithms 1
- Co-authors
- Gian Luca Foresti (5 shared papers)Christian Micheloni (3 shared papers)Carsten Marr (1 shared paper)
- Journals
- International Journal of Neural Systems (2 papers)IEEE Access (1 paper)International Journal of Medical Informatics (1 paper)Institutional Research Information System (University of Udine) (3 papers)
In The Last Decade
Rao Muhammad Umer
7 papers receiving 74 citations
Peers
Comparison fields: 5 of 29
- Computer Vision and Pattern Recognition 42
- Media Technology 17
- Artificial Intelligence 22
- Information Systems 13
- Neurology 3
Countries citing papers authored by Rao Muhammad Umer
This map shows the geographic impact of Rao Muhammad Umer'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 Rao Muhammad Umer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rao Muhammad Umer more than expected).
Fields of papers citing papers by Rao Muhammad Umer
This network shows the impact of papers produced by Rao Muhammad Umer. 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 Rao Muhammad Umer. The network helps show where Rao Muhammad Umer may publish in the future.
Co-authors
The 3 scholars most cited alongside Rao Muhammad Umer, 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 | 35 | |
| 2 | 2023 | 22 | |
| 3 | 2023 | 10 | |
| 4 | 2025 | 4 | |
| 5 | 2024 | 3 | |
| 6 | 2021 | 2 | |
| 7 | 2022 | 1 |
About Rao Muhammad Umer
Rao Muhammad Umer is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Information Systems and Computer Science Applications, having authored 7 papers that have together received 77 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (3 papers), Advanced Image Processing Techniques (3 papers), Advanced Vision and Imaging (2 papers), Image and Signal Denoising Methods (2 papers), Machine Learning and Algorithms (1 paper), Face recognition and analysis (1 paper), Mobile Crowdsensing and Crowdsourcing (1 paper) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (42 citations), Media Technology (17 citations), Artificial Intelligence (22 citations), Information Systems (13 citations) and Neurology (3 citations). Rao Muhammad Umer has collaborated with scholars based in Italy, Germany and Pakistan. Frequent co-authors include Gian Luca Foresti, Christian Micheloni and Carsten Marr. Their work appears in journals such as International Journal of Neural Systems, IEEE Access, International Journal of Medical Informatics and Institutional Research Information System (University of Udine).
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.