Mahmoud Othman
Papers
1
Total Citations
39
H-Index
1
About
Mahmoud Othman is a computer vision researcher whose work focuses on the intersection of deep learning and embedded systems for real-world multimedia applications. His primary research areas include face detection and recognition, image and video processing, and the deployment of convolutional neural networks (CNNs) on low-cost hardware platforms. Othman’s most cited work, "Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi" (2022, 39 citations), addresses the critical challenge of achieving high accuracy and reliability in automated visual recognition systems, particularly as the volume of multimedia content continues to grow exponentially. By integrating CNNs with the Raspberry Pi, he demonstrates a practical, efficient approach to real-time biometric identification that is both cost-effective and scalable. This contribution is especially valuable for robotics and edge computing applications, where system autonomy and resource constraints are paramount. Othman’s research bridges the gap between theoretical advances in deep learning and deployable, real-world solutions, making him a notable figure in applied computer vision.
Research Focus
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Top Papers
- 1