Heejung Shin
Papers
1
Total Citations
3
H-Index
1
About
Heejung Shin is a rising researcher at the intersection of computer vision and embedded systems, with a primary focus on fiducial marker detection and neural network compression. Her most-cited work, "Detection of Fiducial Marker With Neural Network Compression" (2023), addresses a critical challenge in augmented reality, virtual reality, PCB manufacturing, and robot localization: achieving fast, accurate camera positioning without excessive computational burden. By applying neural network compression techniques to fiducial marker detection, Shin has contributed to making these systems more efficient for real-time, resource-constrained environments. Though early in her career, her work has already garnered attention, with her top paper accumulating 3 citations—a solid start for a 2023 publication. This research holds promise for advancing practical applications in AR/VR, industrial automation, and robotics, where precise localization is paramount. Shin’s focus on balancing accuracy with computational efficiency positions her as a thoughtful contributor to the growing field of lightweight computer vision models. Her work is particularly relevant for students and researchers interested in deploying deep learning on edge devices.
Research Focus
Key Achievements
Top Papers
- 1Detection of Fiducial Marker With Neural Network Compression3 citations · 2023