Run Min

Huazhong University of Science and Technology

Papers

Total Citations

H-Index

About

Run Min is a researcher focused on advancing computer vision and robotics through efficient hardware-software co-design. His work centers on developing low-power, high-performance algorithms for real-time image processing, particularly edge detection—a critical component for robot vision systems like motion detection and object tracking. His most cited paper, "Low Computation and High Efficiency Sobel Edge Detector for Robot Vision" (2021, 5 citations), proposes an area- and energy-efficient Sobel edge detection method that balances computational simplicity with accuracy, making it ideal for resource-constrained robotic platforms. This contribution addresses the growing need for lightweight vision algorithms in autonomous systems. While his citation count is modest, Min’s work demonstrates practical impact in embedded vision applications, where efficiency is paramount. His research bridges the gap between theoretical algorithm design and real-world deployment, offering solutions that reduce power consumption without sacrificing performance. For students and researchers exploring edge computing or robotics, Min’s work exemplifies how targeted optimizations can enable smarter, faster, and more autonomous machines.