A list,

In the field of computer vision and image processing, the maximum inter-class variance method (OTSU), also known as OtsU method, is proposed by Japanese scholar Otsu in 1979, is an adaptive threshold segmentation method, reduce the gray image grade into a binary image. The algorithm assumes that the image is divided into two categories (in accordance with the bimodal histogram distribution, the two categories are called foreground/target pixels and background pixels respectively), and then calculates an optimal threshold that will divide the image into two categories to maximize the inter-class variance. Otsu is a one-dimensional representation of Fisher’s discrete judgment analysis.

2 methods





Ii. Source code

function varargout = experiment3(varargin)
% EXPERIMENT3 MATLAB code for experiment3.fig
%      EXPERIMENT3, by itself, creates a new EXPERIMENT3 or raises the existing
%      singleton*.
%
%      H = EXPERIMENT3 returns the handle to a new EXPERIMENT3 or the handle to
%      the existing singleton*.
%
%      EXPERIMENT3('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in EXPERIMENT3.M with the given input arguments.
%
%      EXPERIMENT3('Property'.'Value',...). creates anew EXPERIMENT3 or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before experiment3_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to experiment3_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 experiment3

% Last Modified by GUIDE v2. 5 31-May- 2018. 16:55:57

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @experiment3_OpeningFcn, ...
                   'gui_OutputFcn',  @experiment3_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 experiment3 is made visible.
function experiment3_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 experiment3 (see VARARGIN)

% Choose default command line output for experiment3
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes experiment3 wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = experiment3_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;



function 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 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)
shiyan3 = rgb2gray(imread('shiyan3.bmp'));
size_filter_m = str2double(get(handles.edit1,'string'));
size_filter_n = str2double(get(handles.edit2,'string'));
if isnan(size_filter_m)
    size_filter_m = 3;
end
if isnan(size_filter_n)
    size_filter_n = 3; end shiyan3_filter = medfilt2(shiyan3,[size_filter_m,size_filter_n]); % median filter threshold = graythresh(shiyan3_filter); % Find the threshold shiyan3_seg = im2BW (shiyan3_filter,threshold); Axes (handles. Axes1); imshow(shiyan3); title('original');
axes(handles.axes2);
imshow(shiyan3_filter);
title('Median filtering');
axes(handles.axes3);
imshow(shiyan3_seg);
title('Inter-class variance threshold Segmentation');

% --- 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)
shiyan3 = rgb2gray(imread('shiyan3.bmp'));
size_filter_m = str2double(get(handles.edit1,'string'));
size_filter_n = str2double(get(handles.edit2,'string'));
if isnan(size_filter_m)
    size_filter_m = 3;
end
if isnan(size_filter_n)
    size_filter_n = 3; end shiyan3_filter = medfilt2(shiyan3,[size_filter_m,size_filter_n]); % median filter threshold = graythresh(shiyan3_filter); % Find the threshold shiyan3_seg = im2BW (shiyan3_filter,threshold); Axes (handles. Axes1); imshow(shiyan3); title('original');
axes(handles.axes2);
imshow(shiyan3_filter);
title('Median filtering');
axes(handles.axes3);
imshow(shiyan3_seg);
title('Inter-class variance threshold Segmentation');

se = strel('disk'.1); Shiyan3_erode1 = imerode(shiyan3_seg, SE); % corrosion axes (handles. Axes4); imshow(shiyan3_erode1); title('Corrosion: r=1');

se = strel('diamond'.8);
shiyan3_erode2 = imerode(shiyan3_seg,se);
axes(handles.axes5);
imshow(shiyan3_erode2);
title('Corrosion: r=8');

se = strel('disk'.8);
shiyan3_erode3 = imerode(shiyan3_seg,se);
axes(handles.axes6);
imshow(shiyan3_erode3);
title('Corrosion: r=8');


% --- 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)
shiyan3 = rgb2gray(imread('shiyan3.bmp'));
size_filter_m = str2double(get(handles.edit1,'string'));
size_filter_n = str2double(get(handles.edit2,'string'));
if isnan(size_filter_m)
    size_filter_m = 3;
end
if isnan(size_filter_n)
    size_filter_n = 3;
end
Copy the code

3. Operation results

Fourth, note

Version: 2014 a