四元共空间特征提取算法及其在纸币识别中的应用

盖杉

doi:10.11959/j.issn.1000-436x.2018285

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四元共空间特征提取算法及其在纸币识别中的应用

学术论文 | 更新时间：2024-06-05

- 四元共空间特征提取算法及其在纸币识别中的应用
- Feature extraction algorithm based on quaternion common spatial pattern for banknote recognition
- 通信学报 2018年39卷第12期页码：40-46
- 作者机构：
  
  南昌航空大学信息工程学院，江西南昌 330063
- 作者简介：
  
  [ "盖杉（1980–），男，吉林长春人，博士，南昌航空大学副教授，主要研究方向为图像处理、稀疏表示与模式识别。" ]
- 基金信息：
  
  国家自然科学基金资助项目(No.61563037);江西省杰出青年基金资助项目(No.20171BCB23057);江西省自然科学基金资助项目(No.20171BAB202018)
- DOI：10.11959/j.issn.1000-436x.2018285
  中图分类号： TN92
- 网络出版日期：2018-12，
  
  纸质出版日期：2018-12-25
- 稿件说明：
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盖杉. 四元共空间特征提取算法及其在纸币识别中的应用[J]. 通信学报, 2018,39(12):40-46.

Shan GAI. Feature extraction algorithm based on quaternion common spatial pattern for banknote recognition[J]. Journal on communications, 2018, 39(12): 40-46.
盖杉. 四元共空间特征提取算法及其在纸币识别中的应用[J]. 通信学报, 2018,39(12):40-46. DOI： 10.11959/j.issn.1000-436x.2018285.

Shan GAI. Feature extraction algorithm based on quaternion common spatial pattern for banknote recognition[J]. Journal on communications, 2018, 39(12): 40-46. DOI： 10.11959/j.issn.1000-436x.2018285.

摘要

针对目前纸币图像特征提取与分析方法缺少对相位结构信息有效描述的问题，提出一种基于四元共空间模式的特征提取算法。该算法首先采用四元矩阵描述纸币图像的相位与幅值信息，并对四元复合厄米特协方差矩阵进行对角化；然后将样本向量输入到复合四元滤波器中，并将分解得到的四元矩阵实部与虚部的方差作为纸币特征向量；最后采用神经网络作为分类器并在识别阶段引入拒识类。实验结果表明所提的算法不仅取得了较高的识别率，而且还能够满足纸币清分系统的实时性要求。

Abstract

New feature extraction algorithm was proposed based on quaternion common spatial pattern in order to solve the lack of effective description of phase information in the banknote feature extraction and analysis. Firstly

the quaternion matrix was utilized to describe the phase information of the banknote image

and made diagonalization of quaternion composite Hermitian matrix. Secondly

the sample vector was input to the composite quaternion filter. The extracted feature vector was obtained by using the variance of the real part and imaginary part. Finally

the neural network was applied as classifier and the reject class was introduced in the banknote recognition. The experimental results illustrate that the proposed algorithm obtains high recognition rate and meets the real-time requirement of the banknote recognition system. The proposed algorithm has already been applied in a resource-constrained embedded system at the same time.

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references

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