Amina Radgui
Papers
1
Total Citations
2
H-Index
1
About
Amina Radgui is a computer vision researcher whose work focuses on egomotion estimation for vision-based mobile robotics, particularly leveraging omnidirectional imaging systems. Her key research area centers on developing methods to compute observer motion from visual data, a fundamental challenge for autonomous navigation. Radgui’s major contribution lies in adapting motion field computation for omnidirectional cameras, which offer a large field of view that simplifies egomotion estimation compared to traditional perspective cameras. Her most-cited paper, "Omnidirectional Egomotion Estimation from Adapted Motion Field" (2009), addresses this problem by proposing techniques to compute motion fields directly in the omnidirectional image space, rather than reprojecting to perspective views. While her citation count is modest, this work represents an important step in making omnidirectional vision practical for mobile robot applications, contributing to the broader field of visual odometry and autonomous navigation. Radgui’s research is particularly relevant for students and researchers interested in the intersection of computer vision, robotics, and sensor design, offering insights into how wide-angle imaging can enhance motion perception in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Omnidirectional Egomotion Estimation from Adapted Motion Field2 citations · 2009