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【图像识别】基于BP神经网络和RGB颜色空间实现人民币识别系统matlab代码 1 简介随着信息化时代的到来…

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1 简介

 

随着信息化时代的到来,智能识别成为研究的热点,本文以人民币识别为研究对象,运用 Matlab 软件系统中所提供的神经网络工具箱,结合图像处理技术,实现对各种不同面值纸质版人民币的识别。本文主要针对六种不同面值的纸币,即一元,五元,十元,二十元,五十元,一百元的人民币进行了识别,首先通过样本集构建,图像预处理,特征提取,矩阵转置,样本与样本标签建立一一对应关系,生成相应的训练和测试矩阵,进而通过BP 神经网络对数据矩阵进行分类训练,通过一定的训练次数后,该系统对人民币的识别率可达到 98.33%以上,具有较高的参考价值。纸币的流传已具有悠久的历史,对于纸币的识别方法也已存有很多不同的研究方法,从最初的人工识别到现在的基于信息化的识别方法,它们都存在着自身的优点和缺陷,人工识别过程中,准确率相对较高,但大量的重复操作易使人产生反感和厌恶情绪,同时容易使人产生疲劳感。随着信息化时代的到来,传统识别方法的缺陷就更多的暴露出了出来。运用科学的识别方法解决纸币的识别成为研究的热点。现阶段常用的纸币识别方法主要有尺寸比较法、模板匹配、人工神经网络等。在神经网络系统中,有 BP 网络、RBF 网络、SOM 网络、级联相关网络、Elman 网络、Boltzmann 机等,其中BP 神经网络是较常见的一种,它是一种以误差反向传播为基础的前向网络,具有非常强的非线性映射能力。

 

 

2 部分代码

 

function varargout = main(varargin)
% MAIN MATLAB code for main.fig
%     MAIN, by itself, creates a new MAIN or raises the existing
%     singleton*.
%
%     H = MAIN returns the handle to a new MAIN or the handle to
%     the existing singleton*.
%
%     MAIN('CALLBACK',hObject,eventData,handles,...) calls the local
%     function named CALLBACK in MAIN.M with the given input arguments.
%
%     MAIN('Property','Value',...) creates a new MAIN or raises the
%     existing singleton*. Starting from the left, property value pairs are
%     applied to the GUI before main_OpeningFcn gets called. An
%     unrecognized property name or invalid value makes property application
%     stop. All inputs are passed to main_OpeningFcn via varargin.
%
%     *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
%     instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help main
% Last Modified by GUIDE v2.5 29-May-2020 00:04:07
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
   'gui_Singleton',  gui_Singleton, ...
   'gui_OpeningFcn', @main_OpeningFcn, ...
   'gui_OutputFcn',  @main_OutputFcn, ...
   'gui_LayoutFcn', [] , ...
   'gui_Callback',   []);
if nargin && ischar(varargin{1})
   gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
  [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
   gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before main is made visible.
function main_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject   handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
% varargin   command line arguments to main (see VARARGIN)
% Choose default command line output for main
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes main wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = main_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject   handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject   handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
%% 图像读取
[filename, pathname] = uigetfile({'*.jpg;*.tif;*.png;*.gif','All Image Files';...
   '*.*','All Files' });
l = imread([ pathname,filename]);
axes(handles.axes1)
imshow(l);
title('原始图像')
l1=rgb2gray(l);                %将真彩色图像转换为灰度图像
bw1=edge(l1,'sobel', 'both');  %采用sobel算子进行边缘检测
handles.bw1=bw1;
press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject   handle to pushbutton6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
%% 分割后二值化
I=handles.I4;
axes(handles.axes6)
imshow(I);
title('图像二值化')
% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
% hObject   handle to pushbutton7 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
%% RGB识别
set(handles.text2,'String',['RGB识别结果面额=',num2str(handles.result)])
% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject   handle to pushbutton8 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
%% BP训练
TT=handles.TT;
result1=BP_shibie(TT);
handles.result1=result1;guidata(hObject, handles);
% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject   handle to pushbutton9 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
result1=handles.result1;
set(handles.text2,'String',['BP识别结果面额=',num2str(handles.result1)])

 

3 仿真结果

 

 

 

 

4 参考文献

 

[1]程海玉, & 王辉. (2008). 基于bp神经网络的人民币纸币面额识别方法. 湖北成人教育学院学报, 014(002), 116-封3.

 

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