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Research and Implementation for Intelligent Currency Recognition Method with Applied Neural Networks

应用神经网络智能货币识别方法的研究与实现

基本信息

批准号：
12650413
负责人：
TAKEDA Fumiaki
金额：
$ 2.56万
依托单位：
Kochi University of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12650413/
关键词：
Neural Network Banknote Recognition Pattern Matching DSP Individual Recognition Implementation 紙幣識別自己学習

项目摘要

Up to now, Authors research have been focused on applications of neural networks (NNs) for banknotes recognition. Especially, we have clarified its effectiveness of neurorecognition system on industrial use by implementing the neuro-recognition system to various banking machines in spite of unfocused sensing data and various noised data. Recently, another demand on banknotes recognition will be appeared. Namely, it need the more number of banknotes to be recognized at the same time on the banking machines according to appearance of Euro banknotes. In order to solve this problem, we propose a neuro-templates matching method whose templates are constructed by plural NNs. We show its effectiveness by experiments for various foreign banknotes and Euro dummy. Furthermore, we also refer extension of the proposed method to human security using bio-paratnetric information such as palmprints, fingerprints, face image and so on.First stage, we research neuro tejuplate matching as recognition algorithms. Then, we show its effectiveness with various kinds of simulation using foreign banknotes andEuro ones. At the same time, Vie re design DSP board and implement this proposed algorithms to this DSP board. Second stage, we apply this recognition algorithms and DSP board to individual recognition with pen pressure and inspection system for fallen rice. Then we show the effectiveness and usefulness of this algorithm on the real time systems.

到目前为止，作者的研究主要集中在神经网络（NN）在纸币识别中的应用。特别是，我们已经澄清了其有效性的神经识别系统的工业用途，通过实施的神经识别系统，各种银行机器，尽管未集中的传感数据和各种噪声数据。近年来，又出现了对纸币识别的新需求。也就是说，它需要更多的钞票被识别在同一时间在银行机器上根据欧元钞票的外观。为了解决这个问题，我们提出了一种神经模板匹配方法，其模板是由多个神经网络。通过对各种外国纸币和欧元虚拟货币的实验，证明了该方法的有效性。此外，我们还将该方法推广到人体安全领域，利用人体的生物特征信息，如掌纹、指纹、人脸图像等。第一阶段，我们研究了神经模板匹配识别算法。然后，我们用不同的模拟使用外国纸币和欧元的有效性。同时，重新设计了DSP板，并在此DSP板上实现了本文提出的算法。第二阶段，将此识别算法和DSP板应用于笔压个体识别和落米检测系统中。然后在真实的时间系统上证明了该算法的有效性和实用性。