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About

Hirotsugu Okuno is a leading figure in bio-inspired robotics and neuromorphic vision systems, whose work bridges the gap between insect neurobiology and real-time machine perception. His research focuses on developing compact, low-power vision sensors that mimic biological neural circuits for collision avoidance and visual attention. Okuno’s most influential contributions include the design of a mixed analog-digital vision sensor that detects objects on a direct collision course, inspired by the locust’s visual nervous system—a breakthrough that enables robust, real-time collision avoidance in robots. His papers on this topic have garnered over 30 citations collectively, with key works like “A mixed analog–digital vision sensor for detecting objects approaching on a collision course” and “Real-time robot vision for collision avoidance inspired by neuronal circuits of insects” each cited 10 times. He also advanced visual attention systems using artificial retina chips and bottom-up saliency maps, and recently developed an embedded intelligent system for real-time image classification with a neuro-inspired color constancy algorithm. Okuno’s work is notable for its practical hardware implementations, demonstrating how biological principles can be translated into efficient, real-world robotic vision systems.