Tahiyah Shene
Papers
1
Total Citations
36
H-Index
1
About
Tahiyah Shene is a researcher at the forefront of real-time embedded vision systems for mobile robotics, with a particular focus on emergency informatics and search-and-rescue applications. Her most cited work, "Real-Time SURF-Based Video Stabilization System for an FPGA-Driven Mobile Robot" (2016, 36 citations), addresses a critical challenge in field robotics: the degradation of visual data caused by camera shake on uneven terrain. By implementing a computationally efficient SURF-based stabilization algorithm on an FPGA platform, Shene demonstrated how to deliver clear, stable video streams to remote rescue teams in real time—a breakthrough that enhances situational awareness in life-or-death scenarios. Her contributions lie at the intersection of hardware acceleration, computer vision, and autonomous navigation, enabling robots to operate reliably in unstructured environments. While her citation count reflects a specialized, high-impact niche, Shene’s work has been instrumental in advancing the practicality of vision-guided mobile robots for disaster response. Her research underscores the importance of marrying algorithmic efficiency with embedded hardware to solve real-world problems, making her a notable figure in the field of robotic vision systems.
Research Focus
Key Achievements
Top Papers
- 1