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About this paper
Hao Wang, Jianqi Hu, Andrea Morandi, Alfonso Nardi, Fei Xia, Xuanchen Li, Romolo Savo, Qiang Liu and 2 more
The authors develop an optical feature system for classifying hand-sign images. A slab of disordered lithium-niobate nanocrystals scatters 800 nm input light and generates scattered 400 nm second-harmonic light, producing nonlinear features for a digital linear readout. On the 10-category sign-language-digits test, nonlinear features achieve 85.96% accuracy versus 74.82% with linear features, at 3,500 features.
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