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、不变矩验证码识别简介1 背景意义验证码具有多变性特点而当前的识别系统往往具有很强的针对性 只能够识别某种类型的验证码。随着网络安全技术及验证码生成技术的不断发展已经出现了更加复杂的验证码生成方法如基于动态图像的验证码系统等。运用计算机视觉、模式识别等相关理论对多种不同类型的验证码进行识别研究。矩特征主要表征了图像区域的几何特征,又称为几何矩 由于其具有旋转、平移、尺度等特性的不变特征所以又称其为不变矩。2 理论基础本案例提出了以处理颜色加噪的数字字符为理论研究素材 将模板匹配作为基本框架的验证码识别系统。本系统的优点在于能够对特定类型的数字验证码进行精确识别实验中识别准确率可达到95以上并提供动态更新样本库的功能可根据实际运行的环境提高验证码的识别率。本案例选择了经典的数字验证码识别作为识别的对象。验证码的识别涉及图像预处理、分割、特征提取、识别等相关技术本案例通过对彩色验证码图像进行灰度化、二值化、去噪和归一化等步骤来进行预处理通过建立模板库的动态更新机制来提高系统的兼容性进一步提升验证码识别的效率和准确性。流程图如下3 算法流程为了进行验证码识别 建立模板库建立动态模板库加入自动更新的功能提高对数字验证码的识别率。数字验证码待识别对象即数字0 ~ 9调用mkdir 函数来自动建立模板数据库文件夹。由于图中有类似于椒盐噪声的带有颜色的噪声数字验证码中的数字个数保持常量并且分布均匀、易于分割。因此为了进行下一步的数字分割操作需要先进行验证码图像去噪步骤。本案例采用颜色空间转换到hsv 的方式用阈值过滤的手段进行去噪。进行图像去噪之后可以发现验证码图像中的数字呈现分布均匀的状态并且未明显受到倾斜、形变等因素的影响。借用数字图像二值化、积分投影的方式可对数字进行定位。在实际处理过程中为了保证分割有效加入了区域面积滤波操作将剩余的噪声点进行滤除。为了进行下一步的识别操作需要将分割结果进行归一化处理 整合成固定维数的数字集合。数字图像分割完成后通过提取数字图像的不变矩特征信息与模板库中的数字图像进行对比得到识别结果。⛄二、部分源代码function varargout daunxujian1(varargin)% DAUNXUJIAN1 MATLAB code for daunxujian1.fig% DAUNXUJIAN1, by itself, creates a new DAUNXUJIAN1 or raises the existing% singleton*.%% H DAUNXUJIAN1 returns the handle to a new DAUNXUJIAN1 or the handle to% the existing singleton*.%% DAUNXUJIAN1(‘CALLBACK’,hObject,eventData,handles,…) calls the local% function named CALLBACK in DAUNXUJIAN1.M with the given input arguments.%% DAUNXUJIAN1(‘Property’,‘Value’,…) creates a new DAUNXUJIAN1 or raises the% existing singleton*. Starting from the left, property value pairs are% applied to the GUI before daunxujian1_OpeningFcn gets called. An% unrecognized property name or invalid value makes property application% stop. All inputs are passed to daunxujian1_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 daunxujian1% Last Modified by GUIDE v2.5 23-Oct-2020 21:52:34% Begin initialization code - DO NOT EDITgui_Singleton 1;gui_State struct(‘gui_Name’, mfilename, …‘gui_Singleton’, gui_Singleton, …‘gui_OpeningFcn’, daunxujian1_OpeningFcn, …‘gui_OutputFcn’, daunxujian1_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 daunxujian1 is made visible.function daunxujian1_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 daunxujian1 (see VARARGIN)% Choose default command line output for daunxujian1handles.output hObject;handles.Result [];handles.File [];% Update handles structureguidata(hObject, handles);clc; warning off all;InitAxes(handles);% UIWAIT makes daunxujian1 wait for user response (see UIRESUME)% uiwait(handles.figure1);% — Outputs from this function are returned to the command line.function varargout daunxujian1_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)[filename, pathname] uigetfile({‘.jpg;.tif;.png;.gif’,‘All Image Files’;…‘.’,‘All Files’ },‘载入图像’,…fullfile(pwd, ‘images’));I imread(fullfile(pathname, filename));Result Process_Main(I);handles.File fullfile(pathname, filename);handles.Result Result;guidata(hObject, handles);InitAxes(handles)axes(handles.axes1); imshow(handles.Result.Image); title(‘原图像’);% — Executes on button press in pushbutton2.function pushbutton2_Callback(hObject, eventdata, handles)% hObject handle to pushbutton2 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)if ~isempty(handles.Result)axes(handles.axes1); imshow(handles.Result.Image); title(‘原图像’);axes(handles.axes2); imshow(handles.Result.Medfilt); title(‘中值滤波图像’);end% — Executes on button press in pushbutton3.function pushbutton3_Callback(hObject, eventdata, handles)% hObject handle to pushbutton3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)if ~isempty(handles.Result)axes(handles.axes1); imshow(handles.Result.Image); title(‘原图像’);axes(handles.axes2); imshow(handles.Result.Medfilt); title(‘中值滤波图像’);axes(handles.axes3); imshow(handles.Result.Normalize); title(‘归一化图像’);end% — Executes on button press in pushbutton4.function pushbutton4_Callback(hObject, eventdata, handles)% hObject handle to pushbutton4 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)if ~isempty(handles.Result)axes(handles.axes1); imshow(handles.Result.Image); title(‘原图像’);axes(handles.axes2); imshow(handles.Result.Medfilt); title(‘中值滤波图像’);axes(handles.axes3); imshow(handles.Result.Normalize); title(‘归一化图像’);axes(handles.axes4); imshow(handles.Result.Bww); title(‘二值化图像’);end% — Executes on button 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)if ~isempty(handles.Result)axes(handles.axes1); imshow(handles.Result.Image); title(‘原图像’);axes(handles.axes2); imshow(handles.Result.Medfilt); title(‘中值滤波图像’);axes(handles.axes3); imshow(handles.Result.Normalize); title(‘归一化图像’);axes(handles.axes4); imshow(handles.Result.Bww); title(‘二值化图像’);axes(handles.axes6); imshow(handles.Result.Thin); title(‘细化处理’);axes(handles.axes5); imshow(handles.Result.Bw); title(‘特征提取’);hold on;h [];for i 1 : length(handles.Result.hs) %绘制水平线h [h plot([1 handles.Result.sz(2)], [handles.Result.hs(i) handles.Result.hs(i)], ‘r-’)];endfor i 1 : length(handles.Result.vs)%绘制竖直线h [h plot([handles.Result.vs(i) handles.Result.vs(i)], [1 handles.Result.sz(1)], ‘g-’)];endh [h plot(handles.Result.x1, handles.Result.y1, ‘y-’)];%绘制左对角线h [h plot(handles.Result.x2, handles.Result.y2, ‘m-’)];%绘制右对角线end% — 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)[filename, pathname] uiputfile({‘.jpg;.tif;.png;.gif’,‘All Image Files’;…‘.’,‘All Files’ },‘Save Image’,…fullfile(pwd, ‘Result/result.png’));if ~isequal(filename, 0)imwrite(handles.Result.label, fullfile(pathname, filename));msgbox(‘保存图像成功’, ‘信息提示框’);end% — 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)choice questdlg(‘确定退出?’, …‘退出’, …‘是’,‘否’,‘否’);switch choicecase ‘是’close;otherwisereturn;endfunction edit1_Callback(hObject, eventdata, handles)% hObject handle to edit1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,‘String’) returns contents of edit1 as text% str2double(get(hObject,‘String’)) returns contents of edit1 as a double% — Executes during object creation, after setting all properties.function edit1_CreateFcn(hObject, eventdata, handles)% hObject handle to edit1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc isequal(get(hObject,‘BackgroundColor’), get(0,‘defaultUicontrolBackgroundColor’))set(hObject,‘BackgroundColor’,‘white’);end% — 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)set(handles.edit1,‘string’,handles.Result.label);% — Executes on button press in pushbutton10.function pushbutton10_Callback(hObject, eventdata, handles)% hObject handle to pushbutton10 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)if ~isempty(handles.Result)axes(handles.axes1); imshow(handles.Result.Image); title(‘原图像’);axes(handles.axes2); imshow(handles.Result.Medfilt); title(‘中值滤波图像’);axes(handles.axes3); imshow(handles.Result.Normalize); title(‘归一化图像’);axes(handles.axes4); imshow(handles.Result.Bww); title(‘二值化图像’);axes(handles.axes6); imshow(handles.Result.Thin); title(‘细化处理’);end⛄三、运行结果⛄四、matlab版本及参考文献1 matlab版本2014a2 参考文献[1]赵杰.不变矩特征在数字验证码识别中的应用[J].舰船电子工程. 2018,38(06)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