Ibrahim Sobh
- Computer Vision and Pattern Recognition top 10%
- Automotive Engineering top 10%
- Artificial Intelligence
- Control and Systems Engineering
- Aerospace Engineering
- Co-authors
- Senthil YogamaniPatrick PérezPatrick MannionBangalore Ravi KiranAhmad El SallabMichal UřičářPatrick DennyVarun Ravi Kumar
- Topics
- Gaze Tracking and Assistive Technology (3 papers)Autonomous Vehicle Technology and Safety (3 papers)Optimization and Search Problems (2 papers)
In The Last Decade
Ibrahim Sobh
14 papers receiving 151 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 77
- Automotive Engineering 59
- Artificial Intelligence 54
- Control and Systems Engineering 32
- Aerospace Engineering 15
Countries citing papers authored by Ibrahim Sobh
This map shows the geographic impact of Ibrahim Sobh'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 Ibrahim Sobh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ibrahim Sobh more than expected).
Fields of papers citing papers by Ibrahim Sobh
This network shows the impact of papers produced by Ibrahim Sobh. 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 Ibrahim Sobh. The network helps show where Ibrahim Sobh may publish in the future.
Co-authorship network of co-authors of Ibrahim Sobh
This figure shows the co-authorship network connecting the top 25 collaborators of Ibrahim Sobh. A scholar is included among the top collaborators of Ibrahim Sobh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ibrahim Sobh. Ibrahim Sobh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 0 | |
| 7 | 14 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 33 | |
| 11 | 26 | |
| 12 | 23 | |
| 13 | End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving | 27 |
| 14 | YOLO4D: A Spatio-temporal Approach for Real-time Multi-object Detection and Classification from LiDAR Point Clouds | 18 |
| 15 | 4 |
About Ibrahim Sobh
Ibrahim Sobh is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Automotive Engineering, having authored 15 papers that have together received 159 indexed citations. Recurring topics across this work include Gaze Tracking and Assistive Technology (3 papers), Autonomous Vehicle Technology and Safety (3 papers) and Optimization and Search Problems (2 papers). The work is most often cited by research in Automotive Engineering (59 citations), Computer Vision and Pattern Recognition (77 citations) and Computer Graphics and Computer-Aided Design (7 citations). Ibrahim Sobh has collaborated with scholars based in Egypt, France and Ireland. Frequent co-authors include Senthil Yogamani, Patrick Pérez, Patrick Mannion, Bangalore Ravi Kiran, Ahmad El Sallab, Michal Uřičář, Patrick Denny, Varun Ravi Kumar, Pavel Křížek and Mahmoud S. R. Saeed. Their work appears in journals such as Communications of the ACM, IEEE Access and Neural Computing and Applications.
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.