欢迎来到海神之光博客之家✅博主简介热爱科研的Matlab仿真开发者修心和技术同步精进个人主页海神之光代码获取方式海神之光Matlab王者学习之路—代码获取方式⛳️座右铭行百里者半于九十。更多Matlab图像处理仿真内容点击①Matlab图像处理进阶版②付费专栏Matlab图像处理初级版⛳️关注CSDN海神之光更多资源等你来⛄一、简介本设计为基于MATLAB的人民币识别系统。带有一个GUI界面。先利用radon进行倾斜校正根据不同纸币选择不同维度的参数识别纸币金额有通过RGB分量识别100元通过面额图像的宽度识别1元、5元通过构建矩形结构体识别10元 通过RGB分量识别 20元 与 50元。⛄二、部分源代码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 EDITgui_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});endif nargout[varargout{1:nargout}] gui_mainfcn(gui_State, varargin{:});elsegui_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 mainhandles.output hObject;% Update handles structureguidata(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 structurevarargout{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’)l1rgb2gray(l); %将真彩色图像转换为灰度图像bw1edge(l1,‘sobel’, ‘both’); %采用sobel算子进行边缘检测handles.bw1bw1;theta0:179; %定义theta角度范围rradon(bw1,theta); %对图像进行Radon变换%%%%%检测Radon变换矩阵中的峰值所对应的列坐标%%%%[m,n]size®;c1;for i1:mfor j1:nif r(1,1)r(i,j)r(1,1)r(i,j);cj;endendendrot90-c;picimrotate(l,rot,‘crop’); %对图片进行旋转矫正handles.picpic;pic_grayrgb2gray(pic); %转换为灰度图像handles.pic_graypic_gray;pic_aimadjust(pic_gray,[0,0.001],[1,0]); %明暗反转pic_b1.3pic_gray0.7pic_a;pic_cimadjust(pic_b,[0.5,1],[0,1]); %明暗反转handles.pic_cpic_c;pic_b_edgeedge(pic_c,‘sobel’); %采用sobel算子进行边缘检测handles.pic_b_edgepic_b_edge;se[1;1;1]; %线型结构元素pic_imerodeimerode(pic_b_edge,se); %腐蚀图像handles.pic_imerodepic_imerode;sestrel(‘rectangle’,[60,60]); %矩形结构元素pic_imcloseimclose(pic_imerode,se); %图像聚类、填充图像handles.pic_imclosepic_imclose;pic_bwareaopenbwareaopen(pic_imclose,10000); %去除聚团灰度值小于10000的部分%%%%%求纸币行起始位置和终止位置%%%%%[y,x]size(pic_bwareaopen);I6double(pic_bwareaopen);Y1zeros(y,1);for i1:yfor j1:xif(I6(i,j,1)1)Y1(i,1) Y1(i,1)1;endendend[temp MaxY]max(Y1);%%%%%%%%求纸币列起始位置和终止位置%%%%%PY1MaxY;while ((Y1(PY1,1)50)(PY11))PY1PY1-1;endPY2MaxY;while ((Y1(PY2,1)50)(PY2y))PY2PY21;endIYpic(PY1:PY2,:;X1zeros(1,x);for j1:xfor iPY1:PY2if(I6(i,j,1)1)X1(1,j) X1(1,j)1;endendend%%%%提取并画出背景中的RMB图像%%PX11;while ((X1(1,PX1)❤️)(PX1x))PX1PX11;endPX2x;while ((X1(1,PX2)❤️)(PX2PX1))PX2PX2-1;enddwpic(PY1:PY2,PX1:PX2,:);dw_grayrgb2gray(dw);dw_grayimadjust(dw_gray,[0,1],[1,0]);dw_bwim2bw(dw_gray);handles.dw_bwdw_bw;%%%%分割提取RMB数值图像%%[m,n]size(dw_bw);m1round(m/3);m2round(2m/3);n1round(n/6);n2round(n/3);n3round(2n/3);n4round(5*n/6);sum1sum(sum(dw_bw(m1:m2,n1:n2)));sum2sum(sum(dw_bw(m1:m2,n3:n4)));if sum1sum2dwimrotate(dw,180,‘crop’);end%%%%图像处理%%xdw;x1imresize(x,[236,500]);%缩放图像zimcrop(x1,[270,150,160,65]);%对图像进行剪切选取有效区域%%Iimcrop(x1,[130,60,130,65]); %对图像进行剪切选取有效区域handles.II;I1rgb2gray(I); %转换为灰度图像I2medfilt2(I1); %滤波默认窗口I3imadjust(I2,[0.3,0.5],[0,1],1); %明暗反转I4im2bw(I3);handles.I4I4;sestrel(‘rectangle’,[3,3]); %构造结构函数以长方形构造一个se⛄三、运行结果⛄四、matlab版本及参考文献1 matlab版本2014a2 参考文献[1]陈铭.基于特征的BP神经网络人民币号码识别系统[J].测控技术. 2014,33(12)3 备注简介此部分摘自互联网仅供参考若侵权联系删除 仿真咨询1 各类智能优化算法改进及应用生产调度、经济调度、装配线调度、充电优化、车间调度、发车优化、水库调度、三维装箱、物流选址、货位优化、公交排班优化、充电桩布局优化、车间布局优化、集装箱船配载优化、水泵组合优化、解医疗资源分配优化、设施布局优化、可视域基站和无人机选址优化2 机器学习和深度学习方面卷积神经网络CNN、LSTM、支持向量机SVM、最小二乘支持向量机LSSVM、极限学习机ELM、核极限学习机KELM、BP、RBF、宽度学习、DBN、RF、RBF、DELM、XGBOOST、TCN实现风电预测、光伏预测、电池寿命预测、辐射源识别、交通流预测、负荷预测、股价预测、PM2.5浓度预测、电池健康状态预测、水体光学参数反演、NLOS信号识别、地铁停车精准预测、变压器故障诊断3 图像处理方面图像识别、图像分割、图像检测、图像隐藏、图像配准、图像拼接、图像融合、图像增强、图像压缩感知4 路径规划方面旅行商问题TSP、车辆路径问题VRP、MVRP、CVRP、VRPTW等、无人机三维路径规划、无人机协同、无人机编队、机器人路径规划、栅格地图路径规划、多式联运运输问题、车辆协同无人机路径规划、天线线性阵列分布优化、车间布局优化5 无人机应用方面无人机路径规划、无人机控制、无人机编队、无人机协同、无人机任务分配6 无线传感器定位及布局方面传感器部署优化、通信协议优化、路由优化、目标定位优化、Dv-Hop定位优化、Leach协议优化、WSN覆盖优化、组播优化、RSSI定位优化7 信号处理方面信号识别、信号加密、信号去噪、信号增强、雷达信号处理、信号水印嵌入提取、肌电信号、脑电信号、信号配时优化8 电力系统方面微电网优化、无功优化、配电网重构、储能配置9 元胞自动机方面交通流 人群疏散 病毒扩散 晶体生长10 雷达方面卡尔曼滤波跟踪、航迹关联、航迹融合