Tzu-Wei Huang

Papers

Total Citations

H-Index

About

Tzu-Wei Huang is a researcher whose work bridges computer vision, robotics, and human-robot interaction. His primary research areas include 3D object recognition, depth sensing, and autonomous robotic systems. Huang’s most cited work, “Chess recognition from a single depth image” (2017, 9 citations), introduces a learning-based method for identifying chess pieces using depth data, integrated into a dual-armed robotic system equipped with an Ensenso N35 Stereo 3D camera. This system is designed to play chess against humans, showcasing a practical application of vision-guided robotics. The contribution is notable for demonstrating how depth imaging can enable robust piece recognition under varying lighting and occlusion conditions, a challenge in traditional 2D approaches. Huang’s work has implications for recreational robotics and assistive technologies, offering a template for interactive systems that require precise object detection. While his citation count reflects a focused, early-career impact, the chess robot project stands out as a creative integration of perception and manipulation, highlighting his ability to translate computer vision research into tangible, engaging robotic experiences.