Tzu-Hsien Hung

Papers

Total Citations

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About

Tzu-Hsien Hung is a researcher specializing in computer vision, fuzzy logic systems, and human-robot interaction. His work focuses on developing explainable artificial intelligence methods for tracking human movement, particularly hand palm tracking from monocular images. Hung's major contribution lies in proposing a novel approach that fuses explainable fuzzy features through fuzzy rule-based systems, enabling three-dimensional hand palm tracking from whole-body standing postures in monocular video. This method stands out for its visual and linguistic explainability, allowing both machines and humans to understand the tracking process. His most-cited paper, "Hand Palm Tracking in Monocular Images by Fuzzy Rule-Based Fusion of Explainable Fuzzy Features With Robot Imitation Application" (2021), has garnered 16 citations, demonstrating its impact in the field. This work has direct applications in robot imitation learning, where robots can observe and replicate human hand movements. Hung's research bridges the gap between interpretable AI and practical robotics, offering transparent solutions for human motion analysis. His contributions are valuable for students and researchers interested in explainable fuzzy systems, computer vision, and human-robot collaboration.