Young Tack Park
Papers
2
Total Citations
10
H-Index
2
About
Young Tack Park is a researcher whose work lies at the intersection of robotics, computer vision, and knowledge representation, with a particular focus on enabling intelligent mobile robots to perceive and understand their environments. His major contributions center on developing ontology-based frameworks that allow robots to model and reason about complex, high-level contextual information from raw visual data. In his most cited work, "Ontological representation of vision-based 3D spatio-temporal context for mobile robot applications" (7 citations), Park proposed a novel context model integrating primitive spatial and temporal cues with higher-level semantic reasoning. This approach enables robots to infer not only simple facts like object location and movement but also hidden contextual relationships, significantly advancing object recognition and autonomous navigation. His subsequent paper, "Ontology-Based Semantic Context Modeling for Object Recognition of Intelligent Mobile Robots" (3 citations), further refined these concepts, demonstrating the practical utility of semantic context in improving robotic perception. Park’s research is notable for bridging the gap between low-level sensor data and high-level symbolic reasoning, offering a pathway toward more adaptive and context-aware robotic systems. His work remains relevant for researchers in robotics, AI, and ontology engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2