论文标题
基于深度学习的幽灵手写数字识别
Ghost Handwritten Digit Recognition based on Deep Learning
论文作者
论文摘要
我们为基于幽灵成像（GI）带有深层神经网络的未知手写数字提供了一种幽灵手写数字识别方法，其中通过余弦变换斑点产生的一些检测信号被用作特征信息和设计深神经网络（DNN）的特征信息（DNN），以及该分类为DNN的输出。结果表明，所提出的方案具有更高的识别精度（模拟的98.14％，实验的92.9％）具有较小的采样比（例如12.76％）。随着采样比的增加，识别准确性大大提高。与使用相同DNN结构的传统识别方案相比，提出的方案具有更好的性能，具有较低的复杂性和非本地性能。拟议的方案为遥感提供了一种有希望的方法。
We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the classification is designed as the output of the DNN. The results show that the proposed scheme has a higher recognition accuracy (as high as 98.14% for the simulations, and 92.9% for the experiments ) with a smaller sampling ratio (say 12.76%). With the increase of the sampling ratio, the recognition accuracy is enhanced greatly. Compared with the traditional recognition scheme using the same DNN structure, the proposed scheme has a little better performance with a lower complexity and non-locality property. The proposed scheme provides a promising way for remote sensing.
