Yuki Fujimura

Nara Institute of Science and Technology

Papers

Total Citations

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About

Yuki Fujimura is a researcher at the forefront of computational imaging and material science, with a specialized focus on leveraging single-photon avalanche diode (SPAD) sensors for advanced optical sensing. Their key research areas include material segmentation, surface classification, and the application of deep learning to transient histogram analysis. Fujimura’s most notable contribution is the pioneering work "Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram," which introduces a novel method for distinguishing materials by analyzing temporal resolution in optical sensing. This approach, utilizing 1-D convolutional neural networks, enables precise surface segmentation and material classification, with significant implications for autonomous systems, robotics, and industrial inspection. Although early in its citation impact, this work has already garnered attention for its innovative fusion of SPAD technology and machine learning. Fujimura’s research bridges the gap between high-speed photonics and practical material identification, offering a pathway to more robust and efficient sensing systems. Their work stands as a testament to the power of combining temporal data with neural architectures, promising to influence future developments in non-contact material analysis and real-time environmental perception.