A list,
Mathematical morphology operations can be divided into binary morphology and gray morphology. Gray morphology is expanded from binary morphology. There are two basic operations of mathematical morphology, namely corrosion and expansion, and corrosion and expansion combine to form open and closed operations. Open operation is first corrosion and then expansion, closed operation is first expansion and then corrosion.
1 binary morphology Roughly speaking, corrosion can make the target area “smaller”, which in essence causes the image boundary to shrink, which can be used to eliminate small and meaningless target objects. The formula is expressed as:
2 expansion Roughly speaking, expansion will “enlarge” the scope of the target area, merge the background points in the target area into the target object, so that the target boundary expands outwards. It can be used to fill some holes in the target area and eliminate the small particle noise contained in the target area.
Before telling the gray value morphology, we make A convention that the area of image A covered by structural element B is denoted as P (take Part meaning).
5 Corrosion of grayscale morphology Then corrosion in grayscale morphology is an operation similar to convolution. The small rectangle formed by subtracting structural element B from P can be assigned to the position of the corresponding origin by taking the minimum value. Let’s look at an example to deepen our understanding of grayscale morphology. Suppose we have the following image A and structural element B:
6 Summary Above introduced the concept of gray morphology, here to say their use. Compared with the original image, the corrosion results in smaller pixels than before, so it is suitable for removing peak noise. As a result of gray expansion, each pixel becomes larger than the previous one, so it is suitable for removing the trough noise.
Ii. Source code
function varargout = untitled(varargin)
% UNTITLED M-file for untitled.fig
% UNTITLED, by itself, creates a new UNTITLED or raises the existing
% singleton*.
%
% H = UNTITLED returns the handle to a new UNTITLED or the handle to
% the existing singleton*.
%
% UNTITLED('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in UNTITLED.M with the given input arguments.
%
% UNTITLED('Property'.'Value',...). creates anew UNTITLED or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before untitled_OpeningFcn gets called. An
% unrecognized property name orinvalid value makes property application % stop. All inputs are passed to untitled_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 untitled
% Last Modified by GUIDE v2. 5 21-May- 2021. 23:07:23
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @untitled_OpeningFcn, ...
'gui_OutputFcn', @untitled_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 untitled is made visible.
function untitled_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 untitled (see VARARGIN)
% Choose default command line output for untitled
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes untitled wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = untitled_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)
global im;
global str;
[filename,pathname]=uigetfile_new({'*. *'},'Select training picture... ');
str=[pathname filename];
im=imread(str);
axes(handles.axes1);
imshow(im);
% --- 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)
global im1;
global str1;
[filename,pathname]=uigetfile_new({'*. *'},'Select test picture... ');
str1=[pathname filename];
im1=imread(str1);
axes(handles.axes2);
imshow(im1);
% --- 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)
global im;
global im1;
global str1;
se1 = strel('disk'.5);
se2 = strel('square'.16);
thresh1 = 0.2;
thresh2 = 0.01;
imtra = im2double(im);
rt = imtra(:,:,1);
gt = imtra(:,:,2);
bt = imtra(:,:,3);
idxr1 = find(rt>0);
idxg1 = find(gt>0);
idxb1 = find(bt>0);
mr1 = mean(rt(idxr1));
mg1 = mean(gt(idxg1));
mb1 = mean(bt(idxb1));
clear idxr1 idxg1 idxb1 idxgr1;
im1=imread(str1);
I1 = im2double(im1);
col = size(I1,2);
rate = 1600/col;
I1 = imresize(I1,rate);
r1 = I1(:,:,1);
g1 = I1(:,:,2);
b1 = I1(:,:,3);
[row1,col1] = size(r1);
r1 = abs(r1 - mr1);
g1 = abs(g1 - mg1);
b1 = abs(b1 - mb1);
x1 = (r1+g1+b1)./3;
figure(1);
imshow(x1,[]);
title(Minus the color component);
x1 = im2bw(x1,10/255);
figure(12);
imshow(x1,[]);
title('Binary image');
x1 = imopen(x1,se1);
x1 = imclose(x1,se2);
figure(2);
imshow(x1,[]);
title('After the open and close operation');
y1 = adjvar(g1);
y1 = im2bw(y1,50/255);
figure(3);
imshow(y1,[]);
title('Calculate pixel variance and binarize');
x1 = -x1+1; x1_labeled = bwlabel(x1); X1_num = Max (Max (x1_labeled)); % displays the original image for later marking figure in the loop (4);
imshow(I1);
title('Recognition result');
for i = 1:x1_num
x1_idx = find(x1_labeled==i);
y1_idx = find(y1(x1_idx)==1); % If the ratio of foreground pixel number to total pixel number of connected region in the current connected region corresponding to the binary image of variance is greater than a certain threshold value of %, it is considered to be Maltang grass and marked on the original image. The position to be marked is not accurately calculated here.if(size(y1_idx,1)/size(x1_idx,1)>thresh1)
idx = x1_idx(floor(1+size(x1_idx,1) /2));
r = mod(idx,row1);
c = ceil(idx/row1);
text(c,r,'Horseweed'.'color'.'black');
end
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)
clear all
clc
close(gcf)
Copy the code3. Operation results
Fourth, note
Version: 2014 a