Digit classification of ghost imaging based on similarity measures

Abstract

In this paper, a pattern recognition scheme based on ghost imaging (GI) system is proposed, which is inspired by the fact that the one-dimensional vectors obtained by the GI system can be considered as an alternative representation of the original object, and these vectors will be able to greatly preserve the original object features when appropriate illumination patterns are adopted. The characteristics of GI is combined with discrete Fourier transform (DFT) to generate illumination patterns based on the DFT matrix, which are used to illuminate objects. The obtained bucket detector measurements are utilized to perform similarity metrics and output classification results. The scheme can classify objects with no need of reconstruction and neural network, thus reducing the running time. The experimental and numerical simulation results demonstrate that the proposed scheme requires less computation but has high recognition accuracy, and is also robust to environment noise. In addition, the recognition accuracy of the proposed scheme can also be well maintained when the sampling rate is 50%.