Kyosuke Tokuhara
Papers
1
Total Citations
4
H-Index
1
About
Kyosuke Tokuhara is a robotics researcher whose work centers on enabling autonomous mobile robots to perceive and understand their environments. His primary research areas include semantic scene recognition, visual saliency, and feature-based object detection. Tokuhara’s most notable contribution is a novel method for semantic indoor scene recognition, which integrates accelerated KAZE (AKAZE) features with saliency maps to select the most informative visual cues. By employing self-organizing maps (SOMs) to create bags of visual words, his approach allows robots to efficiently classify and navigate complex indoor spaces. This work, published in 2017, has garnered 4 citations, reflecting its niche but foundational impact in the field of mobile robotics. Tokuhara’s focus on part-based features and visual saliency highlights his commitment to developing robust, biologically inspired algorithms that bridge computer vision and autonomous navigation. His research continues to influence how robots interpret cluttered, real-world environments, making him a promising figure in the advancement of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1