Papers
3
Total Citations
64
H-Index
3
About
Dae Jin Kim is a researcher whose work sits at the intersection of robotics, computer vision, and human-machine interaction, with a particular focus on enabling robots to perceive and respond intelligently to their environments and human users. His most influential contribution, a 2011 study on elevator button recognition garnering 34 citations, tackled the challenging real-world problem of equipping robotic arms to reliably identify and manipulate elevator buttons despite partial occlusion and specular reflections from mirrored surfaces — conditions that confound conventional vision systems. This work exemplifies his commitment to bridging the gap between laboratory robotics and practical deployment. Kim has also made notable strides in affective computing and human-robot interaction. His 2004 research on fuzzy neural network-based facial expression recognition, cited 26 times, introduced a novel feature selection method designed to account for individual differences among people — a frequently overlooked challenge that limits real-world applicability. Complementing this, his work on multi-sensor intention reading employs soft computing techniques to help service robots infer user intent, a capability especially meaningful for assisting individuals with disabilities. Collectively, Kim's research advances the vision of robots that are not merely functional, but genuinely perceptive and responsive partners in human environments.
Research Focus
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Top Papers
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