The original information has a loss because paper currency will get to be worn and blurry , even damaged by human being in circulation . furthermore the complexity of background designs of different kinds of rmb makes it difficult for pattern recognition to work well to the currency recognition 而在货币识别中如何提取高质量的货币特征向量是目前待解决的问题，特别是在具有噪声及灰度不均匀的货币图像中提取出原始的特征信息，是货币识别的关键性步骤。
Now the main methods for paper currency recognition have geometrical dimension measure , feature recognition of image texture and nn etc . nn which is nonlinear math mode is used widely in the application field of currency recognition incorporating with some optimizing arithmetic such as genetic algorithm and simulated anneal in order to optimize nn structure and improve nn recognition precision 主要研究内容包括人民币图像显性特征的提取，人民币图像中噪声信息的抑制和显性特征的加强，识别系统中的数学模型的选取和建立以及优化既得的人民币显性特征等方面。
Finally the vectors optimized by ga are as the input vectors to nn , carrying out classifying recognition by three layer bp nn , paper currency is recognized . by experimental tests , recognition ratio to the new printing style of 100 rmb , the old printing style of 50 rmb , the new printing style of 50 rmb , 20 rmb , the new printing style of 10 rmb is 100 % , 100 % , 100 % , 100 % and 100 % respectively . and the results are satisfying 经过实验测试，对100元新版人民币样本的识别率为100 、 50元新版人民币样本的识别率为100 、 50元旧版人民币样本的识别率为100 、 20元人西安理工大学硕士学位论文民币样本的识别率为100 % 、 10元新版人民币样本的识别率为1加% ，取得了较为满意的结果。
First classifier chooses two classes whose matching distance between it and paper currency is bigger than others from all class . then in the second stage , we extract some new feature and improve the classifier to generate the last result . in the stage of defect detection for paper currency , we advances a homogeneity based algorithm for the detection of scratch and cracks appearing on paper currency , in which the homogeneity feature of the sensed paper currency image is first constructed to locate the pixels that probably been polluted , the image registration algorithm is subsequently used to overlay the sensed and reference paper currency image 在特征提取中,我们对基于方向块的特征提取方法进行了分析,在此基础上针对美元特点,对图像方向块的划分方式做了研究,并提出了基于几何距离的特征提取方法;在分类器设计中,我们采用了lvq网络对纸币进行学习与分类,并提出了一种具有两层结构的分类算法,第一层首先对输入的特征向量进行粗分类,选定与特征向量匹配距离最大的两类币种,进入第二层分类器;在第二层分类器中,我们通过研究进入该模块两类币种特征块的相关性,重新设计了特征向量,同时对分类器进行改进,最终实现对纸币的分类。
We sample from parallel edges in equal distance , and we sample again from sample points set by slope and get fitting points set , then we use least square method to fit straight line with the fitting points set . the edges of paper currency image can be detected accurately and quickly by above method , and it is valid to some image with a certain defection 边缘检测采用了对平行边缘整体等间隔抽样,根据斜率对抽样点集进行再次抽样后得到拟和点集,用最小二乘法根据拟和点集拟和直线的方法,可以快速精确的检测到纸币边缘,并且能够处理边缘有一定程度残缺的纸币图像。
Then in pre - processing stage , for the allowance of the real time demand , we dispose the method of hough transform and apply linear fitting to determine the border of paper currency to achieve localization and adjustment directly . by the analysis of based directional block feature extraction , we propose a new method of feature extraction based on geometry distance . in the design of classifier , we use the lvq network to classify the feature and introduce a classifier with two layers 在图像的预处理中,由于系统对纸币处理实时性的要求,该过程必须在很短时间内完成,因此我们对图像的边界检测,撇弃了诸如图像二值化, hough变换等处理手段,而是直接采用分段线性拟合的方法确定图像中纸币的四个边界,以完成纸币的定位和倾斜矫正。
In chapter three , the theory of negotiable instruments and the theory of counterplea , the author introduces the origin and development of the theory of the world negotiable instrument counterplea , which covers the developments of the solemn theory of negotiable instrument counterplea , from the theory of paper currency to the foundation of the non - causation principle of negotiable instruments , introduces the contrast to contractual theory , to the creative 第四,不同的票据理论,最终要解决的问题都是如何判断某一抗辩的法律或理论构成,有时也会出现殊途同归的效果,但必须注意理论的一贯性。第四章票据抗辩限制侧重对票据抗辩作了制度上的分析,包括票据抗辩制度的特色,票据抗辩限制的立法例、理论根据、前提条件,例外规定等内容。该部分的研
