Kah Eng Hoe

Papers

Total Citations

H-Index

About

Kah Eng Hoe’s research lies at the intersection of computer vision and human-robot interaction (HRI), with a focus on enabling service robots to operate intuitively in human environments. His most cited work, “Lift-button detection and recognition for service robot in buildings” (15 citations), tackles a fundamental challenge for autonomous navigation: enabling robots to detect and press lift buttons using vision, a critical step for multi-floor mobility. Expanding into HRI, his paper on “Head pose estimation in thermal images” (10 citations) addresses the need for fast, robust attention detection, proposing thermal imaging as a solution for real-time interaction. He further advanced natural HRI with “Human Upper Body Pose Recognition Using Adaboost Template” (7 citations), which uses disparity images to classify standing poses into seven view categories, enabling robots to interpret human gestures. His work on “An Interactive Robot Butler” (5 citations) demonstrates the practical integration of these technologies. With over 37 total citations, Kah Eng Hoe’s contributions provide foundational solutions for vision-based robot perception and interaction, directly supporting the development of autonomous service robots in complex, human-centric settings.