简介：
本文主要介绍几种基于灰度的图像匹配算法：平均绝对差算法（MAD）、绝对误差和算法（SAD）、误差平方和算法（SSD）、平均误差平方和算法（MSD）、归一化积相关算法（NCC）、序贯相似性检测算法（SSDA）、hadamard变换算法（SATD）。下面依次对其进行讲解。
MAD算法
介绍
平均绝对差算法（Mean Absolute Differences，简称MAD算法），它是Leese在1971年提出的一种匹配算法。是模式识别中常用方法，该算法的思想简单，具有较高的匹配精度，广泛用于图像匹配。
设S(x,y)是大小为mxn的搜索图像，T(x,y)是MxN的模板图像，分别如下图(a)、(b)所示，我们的目的是：在(a)中找到与(b)匹配的区域（黄框所示）。
算法思路
在搜索图S中，以(i,j)为左上角，取MxN大小的子图，计算其与模板的相似度；遍历整个搜索图，在所有能够取到的子图中，找到与模板图最相似的子图作为最终匹配结果。
MAD算法的相似性测度公式如下。显然，平均绝对差D(i,j)越小，表明越相似，故只需找到最小的D(i,j)即可确定能匹配的子图位置：
其中：
算法评价：
优点：
①思路简单，容易理解（子图与模板图对应位置上，灰度值之差的绝对值总和，再求平均，实质：是计算的是子图与模板图的L1距离的平均值）。
②运算过程简单，匹配精度高。
缺点：
①运算量偏大。
②对噪声非常敏感。
——————————————————————————————————————————————————————————————————————————————
SAD算法
介绍
绝对误差和算法（Sum of Absolute Differences，简称SAD算法）。实际上，SAD算法与MAD算法思想几乎是完全一致，只是其相似度测量公式有一点改动（计算的是子图与模板图的L1距离），这里不再赘述。
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function varargout = homework2(varargin)
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% HOMEWORK2 M-file for homework2.fig
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% HOMEWORK2, by itself, creates a new HOMEWORK2 or raises the existing
-
% singleton*.
-
%
-
% H = HOMEWORK2 returns the handle to a new HOMEWORK2 or the handle to
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% the existing singleton*.
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%
-
% HOMEWORK2('CALLBACK',hObject,eventData,handles,...) calls the local
-
% function named CALLBACK in HOMEWORK2.M with the given input arguments.
-
%
-
% HOMEWORK2('Property','Value',...) creates a new HOMEWORK2 or raises the
-
% existing singleton*. Starting from the left, property value pairs are
-
% applied to the GUI before homework2_OpeningFcn gets called. An
-
% unrecognized property name or invalid value makes property application
-
% stop. All inputs are passed to homework2_OpeningFcn via varargin.
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%
-
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
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% instance to run (singleton)".
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%
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% See also: GUIDE, GUIDATA, GUIHANDLES
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-
% Edit the above text to modify the response to help homework2
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-
% Last Modified by GUIDE v2.5 20-May-2013 21:21:00
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-
% Begin initialization code - DO NOT EDIT
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gui_Singleton =
1;
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gui_State = struct(
'gui_Name', mfilename, ...
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'gui_Singleton', gui_Singleton, ...
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'gui_OpeningFcn', @homework2_OpeningFcn, ...
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'gui_OutputFcn', @homework2_OutputFcn, ...
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'gui_LayoutFcn', [] , ...
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'gui_Callback', []);
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if nargin && ischar(varargin{
1})
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gui_State.gui_Callback = str2func(varargin{
1});
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end
-
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if nargout
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[varargout{
1:nargout}] = gui_mainfcn(gui_State, varargin{:});
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else
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gui_mainfcn(gui_State, varargin{:});
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end
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% End initialization code - DO NOT EDIT
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-
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% --- Executes just before homework2 is made visible.
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function homework2_OpeningFcn(hObject, eventdata, handles, varargin)
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% This function has no output args, see OutputFcn.
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% hObject handle to figure
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% eventdata reserved - to be defined in a future version of MATLAB
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% handles structure with handles and user data (see GUIDATA)
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% varargin command line arguments to homework2 (see VARARGIN)
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% Choose default command line output for homework2
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handles.output = hObject;
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-
% Update handles structure
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guidata(hObject, handles);
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% UIWAIT makes homework2 wait for user response (see UIRESUME)
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% uiwait(handles.figure1);
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-
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% --- Outputs from this function are returned to the command line.
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function varargout = homework2_OutputFcn(hObject, eventdata, handles)
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% varargout cell array for returning output args (see VARARGOUT);
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% hObject handle to figure
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% eventdata reserved - to be defined in a future version of MATLAB
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% handles structure with handles and user data (see GUIDATA)
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-
% Get default command line output from handles structure
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varargout{
1} = handles.output;
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%载入原始身份证图像的回调函数
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% --- Executes on button press in OriginalImg.
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function OriginalImg_Callback(hObject, eventdata, handles)
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% hObject handle to OriginalImg (see GCBO)
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% eventdata reserved - to be defined in a future version of MATLAB
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% handles structure with handles and user data (see GUIDATA)
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[FileName,PathName] = uigetfile(
'*.jpg',
'Select an image');
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if PathName~=
0
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str = [PathName,FileName];
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T=imread(str);
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axes(handles.Img);
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imshow(T);
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end
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%图像自动亮度调整的回调函数
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% --- Executes on button press in autoLight.
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function autoLight_Callback(hObject, eventdata, handles)
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% hObject handle to autoLight (see GCBO)
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% eventdata reserved - to be defined in a future version of MATLAB
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% handles structure with handles and user data (see GUIDATA)
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axes(handles.Img);
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T=getimage;
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low_out=
0.2; high_out=
0.9;
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gamma=
1.518;
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hsv=rgb2hsv(T);
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I=hsv(:,:,
3);
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minL=min(min(I));
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maxL=max(max(I));
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J=imadjust(I,[minL;maxL],[low_out;high_out],gamma);
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hsv(:,:,
3)=J;
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rgb_atuoI=hsv2rgb(hsv);
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axes(handles.Light);
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imshow(rgb_atuoI);
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%图像二值化的回调函数
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% --- Executes on button press in DIP.
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function DIP_Callback(hObject, eventdata, handles)
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% hObject handle to DIP (see GCBO)
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% eventdata reserved - to be defined in a future version of MATLAB
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% handles structure with handles and user data (see GUIDATA)
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axes(handles.Img);
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I=getimage;
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[m,n,r]=size(I);
%图像的像素为width*height
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%%%%%蓝色字体变黑
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myI=double(I);
-
-
for i=
1:m
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for j=
1:n
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if((myI(i,j,
1)>=
15)&&(myI(i,j,
1)<=
130)&&((myI(i,j,
2)<=
165)&&(myI(i,j,
2)>=
90))&&((myI(i,j,
3)<=
220)&&(myI(i,j,
3)>=
135)))
% 蓝色RGB的灰度范围
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I(i,j,
1)=
40;
%红色分量
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I(i,j,
2)=
40;
%绿色分量
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I(i,j,
3)=
40;
%蓝色分量
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end
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end
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end
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%figure, imshow(I);title('变色后的图像');
-
-
width=round(
0.9*n);height=round(
0.87*m);
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rx=round(
0.05*n);cy=round(
0.075*m);
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I=subim(I,height,width,rx,cy);
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%figure,imshow(I);
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-
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if sum(size(I)>
0)==
3
%倘若是彩色图--2维*3，先转换成灰度图
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I=rgb2gray(I);
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end
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%figure,imhist(I);
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x=
3;
%行数分为x部分
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y=
1;
%列数分为y部分
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BW=erzhihua(I,x,y);
-
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[n m l]=size(BW);
%图像的像素为m*n
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c = [
0.65*m
0.65*m m m];
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r = [
0
0.85*n
0.85*n
0];
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BW = roifill(BW,c,r);
-
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BW=imadjust(BW);
%使用imadjust函数对图像进行增强对比度
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% Convert to BW
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threshold = graythresh(BW);
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BW =~im2bw(BW,
0.6*threshold);
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[image_h image_w]=size(BW);
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% Remove all object containing fewer than (imagen/80) pixels
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BW = bwareaopen(BW,floor(image_w/
80));
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% 滤波
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%h=fspecial('average',1);
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%BW=im2bw(round(filter2(h,BW)));
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%imwrite(d,'4.均值滤波后.jpg');
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axes(handles.Binary);
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imshow(BW);
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-
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%图像分割与识别按钮的回调函数
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% --- Executes on button press in OCR.
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function OCR_Callback(hObject, eventdata, handles)
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% hObject handle to OCR (see GCBO)
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% eventdata reserved - to be defined in a future version of MATLAB
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% handles structure with handles and user data (see GUIDATA)
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axes(handles.Binary);
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imagen = getimage;
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[image_h image_w]=size(imagen);
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%figure;imshow(imagen);title('INPUT IMAGE')
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% Convert to gray scale
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if size(imagen,
3)==
3
%RGB image
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imagen=rgb2gray(imagen);
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end
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%Storage matrix word from image
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word=[ ];
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re=imagen;
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%Opens text.txt as file for write
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fid = fopen(
'ID_card.txt',
'wt');
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% Load templates
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load templates
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global templates
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% Compute the number of letters in template file
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num_letras=size(templates,
2);
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figure;
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plot_flag=
1;
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while
1
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%Fcn 'lines' separate lines in text
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[fl re]=lines(re);
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imgn=fl;
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[line_h line_w]=size(fl);
%记录下切割出来的一行字符的长宽
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%Uncomment line below to see lines one by one
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% imshow(fl);pause(1)
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%-----------------------------------------------------------------
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% Label and count connected components
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[L Ne] = bwlabel(imgn);
-
-
n=
1;
%记录循环次数
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while(n<=Ne)
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char_flag=
0;
%为0时，是第一次判断这个连通域
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flag=
1;
%初始化两个连通域属于同个字符
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while(flag==
1)
-
if char_flag==
0
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[r,c] = find(L==n);
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Width0=max(r)-min(r);
%连通域宽度
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Height0=max(c)-min(c);
%连通域高度
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Radio0=Width0/Height0;
%连通域宽高比
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Square0=Width0*Height0;
%连通域面积
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maxr=max(r);
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maxc=max(c);
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minr=min(r);
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minc=min(c);
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end
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if n<Ne
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[r1,c1] = find(L==(n+
1));
%寻找下一个连通域
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Width1=max(r)-min(r);
%连通域宽度
-
Height1=max(c)-min(c);
%连通域高度
-
Radio1=Width1/Height1;
%连通域宽高比
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Square1=Width1*Height1;
%连通域面积
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Uheight=max(maxc,max(c1))-min(minc,min(c1));
%合并后高度
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Uwidth=max(maxr,max(r1))-min(minr,min(r1));
%合并后宽度
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Uradio=Uwidth/Uheight;
%合并后的宽高比
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Oheigth=Height0+Height1-Uheight;
%重叠高度
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Owidth=Width0+Width1-Uwidth;
%重叠宽度
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Osquare=Oheigth*Owidth;
%重叠面积
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else
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flag=
0;
%这是这一行最后一个连通域
-
end
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ph=
5;
%边界因子
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pw=
7;
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if(flag==
1)&&((Owidth>=-(image_w/pw)&&Owidth<=
0)||(Oheigth>=-(line_h*
0.3)&&Oheigth<=
0))
%两个连通域较近，但不重叠
-
if((Uradio>=
0.8)&&(Uradio<=
1.2))
%认为两个连通域属于同一个字符
-
elseif Uheight<line_h*
0.4;
%连通域的合并之后高度过小的，认为是一个字符的一部分，很可能是边旁部首
-
else flag=
0;
%否则这两个连通域属于不同字符
-
end
-
elseif(flag==
1)&&(Owidth<-(image_w/pw))
%两个连通域里相距较远
-
flag=
0;
%两个连通域属于不同字符
-
% elseif(flag==1)&&((Owidth>0)||(Oheigth>0))%两连通域重叠
-
elseif(flag==
1)&&((Owidth>
0))
%两连通域重叠
-
if(((Uradio>=
0.78)&&(Uradio<=
1.3)))
%认为两个连通域属于同一个字符
-
elseif(Osquare>=
0.4*min(Square0,Square1)&&(Uwidth<image_w/
45))
-
else
-
flag=
0;
%两个连通域属于不同字符
-
end
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else flag=
0;
%两个连通域属于不同字符
-
end
-
if flag==
1
%经过上面判断，两个连通域属于同一个字符，进行连通域合并
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Width0=Uwidth;
%连通域宽度
-
Height0=Uheight;
%连通域高度
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Radio0=Width0/Height0;
%连通域宽高比
-
Square0=Width0*Height0;
%连通域面积
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maxr=max(maxr,max(r1));
-
maxc=max(maxc,max(c1));
-
minr=min(minr,min(r1));
-
minc=min(minc,min(c1));
-
n=n+
1;
%指向下一个连通域
-
char_flag=
1;
-
end
-
end
%while(flag==1)的end
-
-
-
-
% Extract letter
-
n1=imgn(minr:maxr,minc:maxc);
-
% Resize letter (same size of template)
-
img_r=imresize(n1,[
36
23]);
-
subplot(
10,
10,plot_flag),imshow(img_r);title(plot_flag);
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plot_flag=plot_flag+
1;
-
%Uncomment line below to see letters one by one
-
% imshow(img_r);title(n);pause(0.5)
-
%-------------------------------------------------------------------
-
% Call fcn to convert image to text
-
letter=read_letter(img_r,num_letras);
-
% Letter concatenation
-
word=[word letter];
-
n=n+
1;
-
end
% while(n<=Ne)的end
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%fprintf(fid,'%s\n',lower(word));%Write 'word' in text file (lower)
-
fprintf(fid,
'%s\n',word);
%Write 'word' in text file (upper)
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% Clear 'word' variable
-
word=[ ];
-
%*When the sentences finish, breaks the loop
-
if isempty(re)
%See variable 're' in Fcn 'lines'
-
break
-
end
-
end
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fclose(fid);
-
%Open 'ID_card.txt' file
-
winopen(
'ID_card.txt')
-
-
-
% --- Executes on button press in Exit.
-
function Exit_Callback(hObject, eventdata, handles)
-
% hObject handle to Exit (see GCBO)
-
% eventdata reserved - to be defined in a future version of MATLAB
-
% handles structure with handles and user data (see GUIDATA)
-
clc;
-
close all;
-
close(gcf);
完整代码添加QQ1575304183
转载：https://blog.csdn.net/weixin_50197058/article/details/116505906
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