Sung Gaun Kim
Papers
2
Total Citations
23
H-Index
2
About
Sung Gaun Kim is a researcher whose work lies at the intersection of computer vision and robotics, with a particular focus on depth estimation and feature matching for stereo vision systems. His most-cited paper, "An improved feature matching technique for stereo vision applications with the use of self-organizing map" (2012, 14 citations), introduces a novel approach that leverages self-organizing maps to enhance the accuracy and robustness of feature correspondence in stereo pairs—a critical step for 3D reconstruction and scene understanding. Building on this, his subsequent work, "Depth estimation of features in video frames with improved feature matching technique using Kinect sensor" (2012, 9 citations), addresses the longstanding challenge of recovering depth from video by integrating active sensing from the Kinect sensor. This technique not only improves depth map quality but also enables real-time gesture and pose recognition, bridging the gap between passive stereo methods and active depth sensors. Kim’s contributions have advanced practical applications in human-computer interaction and autonomous navigation, demonstrating how intelligent feature matching can unlock richer spatial data from affordable hardware.
Research Focus
Key Achievements
Top Papers
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- 2