基于图像统计特征的新旧纸币识别算法

An Recognition Algorithm Based on Statistical Feature for Damaged Degree of Paper Money

如何将旧币分离出来是银行、金融等机构中一项非常重要的工作.针对旧币分离问题,本文提出了基于统计特征的旧币分离算法.首先根据纸币统计特征构建训练样本集,包括纸币灰度值图像的标准差和间断强度等;其次在训练样本集上构建学习向量量化神经网络模型,建立输入与输出之间的关系.仿真实验结果表明,文中算法提高了分离正确率.

How to discriminate the old banknotes from the banknotes is one of important task in financial institution. For the separation of old banknotes,the separation algorithms for the old banknotes was proposed based on statistical feature. Firstly,the training set was formed by the statistical features,which include the standard deviation and the discontinuity degree of the corresponding gray image for banknotes. Secondly,Learning Vector Quantization neural network was used to train the relation between input and output. Experimental results showed that the proposed algorithms can improve the separation accuracy.