1 Introduction

Image segmentation technology is widely used in computer vision, pattern recognition, medical image processing and other fields. Its main purpose is to extract interested objects from an image. It is the basis of image analysis and image understanding. So far, thousands of segmentation algorithms have been proposed, among which the most classical method is the segmentation method based on gray threshold. In image thresholding segmentation, Otsu method... Is widely used because of its simple calculation and high real-time performance. However, the one-dimensional Otsu method only considers the image gray information, which is sensitive to noise and weak in anti noise. In order to solve the problem that the traditional two-dimensional Otsu algorithm has poor segmentation effect in noisy image applications, a two-dimensional Otsu image segmentation algorithm based on median filter is proposed in this paper. The algorithm fully combines the median filter and two-dimensional Otsu algorithm, and makes up for the deficiency of two-dimensional Otsu algorithm in denoising performance. In this paper, the adaptive weighted median filter is used to filter the noisy image; Then, the two-dimensional histogram region division of the filtered median image is changed from the original quartering method to the bisection method, taking full account of each

Based on the information of pixels, the improved two-dimensional Otsu algorithm is used to segment the image accurately, which reduces the computational complexity and improves the practicability of the algorithm.

Considering the problem that the segmentation effect of N-dimensional Otsu algorithm is poor in the application of noisy images, this paper first proposes a median filter, and then uses this filter to filter the noisy images. The filtering algorithm includes three processes:

a) Noise detection for noise image;

b) The size of the filtering window is determined according to the number of noise points in the window;

C) use median filter to filter the noise image.

2 part 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 a new 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 EDITgui_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 experiment3handles.output = hObject;​% Update handles structureguidata(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 structurevarargout{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;endif isnan(size_filter_n)    size_filter_n = 3;​​function edit2_Callback(hObject, eventdata, handles)% hObject    handle to edit2 (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 edit2 as text%        str2double(get(hObject,'String')) returns contents of edit2 as a double​​% --- Executes during object creation, after setting all properties.function edit2_CreateFcn(hObject, eventdata, handles)% hObject    handle to edit2 (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​

3 simulation results

4 references

[1] Ni Lin, Gong Yu, Cao Li, et al Two dimensional Otsu image segmentation algorithm based on adaptive weighted median filter [J] Computer application research, 2013, 30 (2): 3

About the blogger: he is good at matlab simulation in many fields, such as intelligent optimization algorithm, neural network prediction, signal processing, cellular automata, image processing, path planning, UAV, etc. relevant matlab code problems can be exchanged through private letters.

Some theories cite online literature. If there is infringement, contact the blogger to delete it.