Mohamed Hannat
Papers
2
Total Citations
9
H-Index
2
About
Mohamed Hannat’s research lies at the intersection of computer vision and robotics, with a focused expertise in real-time object recognition and categorization for robotic grasping. His major contributions center on developing computationally efficient methods that allow robots to identify and classify objects quickly enough to perform reliable grasps in dynamic environments. In his most cited work, “2D/3D Object Recognition and Categorization Approaches for Robotic Grasping” (2017, 5 citations), Hannat explores how combining two- and three-dimensional visual data can improve a robot’s ability to interact with unfamiliar objects. His earlier paper, “A fast object recognition and categorization technique for robot grasping using the visual bag of words” (2016, 4 citations), introduced a real-time system that uses SURF feature points, K-means clustering to create visual words, and a Support Vector Machine classifier to achieve rapid visual categorization. Though his citation counts are modest, these works represent foundational steps toward making robotic perception faster and more practical for real-world applications. Hannat’s research is particularly valuable for students and engineers working on autonomous manipulation, offering clear, implementable approaches to bridging the gap between visual input and physical action.
Research Focus
Key Achievements
Top Papers
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