【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，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.
