Naoto Ageishi
Papers
1
Total Citations
9
H-Index
1
About
Naoto Ageishi is a researcher whose work sits at the intersection of computer vision and human-computer interaction, with a particular focus on real-time gesture recognition. His most-cited paper, "Real-time Hand-Gesture Recognition based on Deep Neural Network" (2021, 9 citations), addresses a critical challenge in nonverbal communication technology: enabling machines to interpret visible bodily actions for practical applications. Ageishi's contribution lies in developing deep learning architectures capable of processing hand gestures with the speed and accuracy required for real-world deployment, particularly in advanced driver assistance systems. While his citation count reflects an emerging career, the practical implications of his work—enhancing safety and intuitive control in automotive contexts—demonstrate its applied value. Ageishi's research bridges the gap between theoretical deep learning advances and tangible human-machine interfaces, positioning him as a promising voice in the growing field of gesture-based interaction systems.
Research Focus
Key Achievements
Top Papers
- 1Real-time Hand-Gesture Recognition based on Deep Neural Network9 citations · 2021