Image segmentation based algorithm and implementation example

Recent projects involve the field of image processing, a small study, and the collection of data to achieve several basic functions.

First, image inversion

I=imread (' input_image.jpg '); J=double (I); j=-j+ (256-1); % image inversion Linear transform h=uint8 (J); subplot (3,3,4), Imshow (H); title (' Image reversal linear transformation '); axis ([50,250,50,200]); axis on;

TwoGray-scale linear transformation

I=imread (' input_image.jpg '); subplot (3,3,1), imshow (i); title (' Original image '); axis ([50,250,50,200]); axis on;i1 = Rgb2gray (i) ; subplot (3,3,2), Imshow (I1) title (' Grayscale image ') axis ([50,250,50,200]); grid on;axis on; K=imadjust (i1,[0.3 0.7],[]); subplot (3,3,3), Imshow (K); title (' Linear transform image [0.3 0.7] '); axis ([50,250,20,200]); Grid On;axis On

ThreeNonlinear Transformations

I=imread (' input_image.jpg '); I1 = Rgb2gray (I); subplot (3,3,5), Imshow (I1), title (' Grayscale image '), axis ([50,250,50,200]); grid On % display grid line axis on; % Display coordinate system j=double (I1); j=40* (log (j+1)); H=uint8 (J); subplot (3,3,6), Imshow (H); title (' Logarithmic transform image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system

The above code results:

Four, histogram equalization

I=imread (' input_image.jpg '); Figure;i=rgb2gray (i); subplot (2,2,1); imshow (i); Subplot (2,2,2); imhist (I); title (' Histogram Equalization of images '); I1 = Histeq (I); subplot (2,2,3); Imshow (I1); subplot (2,2,4); imhist (I1);

The above code results:

Five, linear smoothing filter

I=imread (' input_image.jpg '); Figure;subplot (231) imshow (i) title (' Original image ') I=rgb2gray (i); I1=imnoise (i, ' Salt & Pepper ', 0.02); subplot (232) Imshow (I1) title (' Add salt and pepper noise image ') K1=filter2 (fspecial (' Average ', 3), I1)/255; % for 3*3 Template Smoothing Filter k2=filter2 (fspecial (' Average ', 5), I1)/255; % for 5*5 Template Smoothing Filter k3=filter2 (fspecial (' average ', 7), I1)/255; % for 7*7 Template Smoothing Filter k4=filter2 (fspecial (' average ', 9), I1)/255; % for 9*9 template Smoothing filter subplot (233), imshow (K1), title (' 3*3 Template Smoothing filter '), subplot (234), Imshow (K2); title (' 5*5 Template smoothing filter '); subplot (235) , Imshow (K3), title (' 7*7 Template Smoothing filter '), subplot (236), Imshow (K4), title (' 9*9 Template smoothing filter ');

The above code results:

VI. Median filter

Figure;i=imread (' input_image.jpg '); I=rgb2gray (i); subplot (231), imshow (i); title (' Original image '); J=imnoise (I, ' salt & Pepper ', 0.02); subplot (232), Imshow (J); title (' Add salt and pepper noise image '); K1=MEDFILT2 (J); % for 3*3 template median filter k2=medfilt2 (j,[5,5]); % for 5*5 template median filter k3=medfilt2 (j,[7,7]); % for 7*7 template median filter k4=medfilt2 (j,[9,9]); % for 9*9 template median filter subplot (233), imshow (K1), title (' 3*3 template median filter '), subplot (234), Imshow (K2); title (' 5*5 template median filter '); subplot (235) , Imshow (K3), title (' 7*7 template median filter '), subplot (236), imshow (K4); title (' 9*9 template median filter ');

The above code results:

Vii. Use ofSobeloperator and Laplace on image sharpening

Figure;i=imread (' input_image.jpg '); subplot (2,2,1), Imshow (I); title (' Original image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I1=IM2BW (I); subplot (2,2,2), Imshow (I1); title (' Binary image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system h=fspecial (' Sobel '); % Select Sobel operator j=filter2 (H,I1); % convolution operation subplot (2,2,3), Imshow (J); title (' Sobel operator sharpening image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % display coordinate system I1 = double (I1); h=[0 1 0,1-4 1,0 1 0]; % Laplace operator J1=conv2 (i1,h, ' same '); % convolution operation subplot (2,2,4), Imshow (J1), title (' Laplace operator sharpening image '), axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system

The above code results:

Eightgradient operator detects edges

Figure;i=imread (' input_image.jpg '); subplot (2,3,1); Imshow (I); title (' Original image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I1=IM2BW (I); subplot (2,3,2); Imshow (I1); title (' Binary image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I2=edge (I1, ' Roberts '); Subplot (2,3,3); Imshow (I2); title (' Roberts operator segmentation result '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I3=edge (I1, ' Sobel '); subplot (2,3,4); Imshow (I3); title (' Sobel operator segmentation result '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I4=edge (I1, ' Prewitt '); subplot (2,3,5); Imshow (I4); title (' Prewitt operator segmentation result '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system

NineLOGoperator Detection Edge

I1=rgb2gray (I); I2=edge (I1, ' log '), subplot (2,3,6); Imshow (I2); title (' Log operator segmentation result ');

The above code results:

TenCannyoperator Detection Edge

Figure;i=imread (' input_image.jpg '); subplot (2,2,1); imshow (i); title (' Original image ') I1=rgb2gray (i); subplot (2,2,2); Imshow ( I1); title (' Grayscale image '); I2=edge (I1, ' canny '); subplot (2,2,3); Imshow (I2); title (' Canny operator segmentation result ');

The above code results:

ElevenBoundary Tracking (bwtraceboundaryfunction)

I=imread (' input_image.jpg '); Figuresubplot (2,2,1); Imshow (I); title (' Original image '); I1=rgb2gray (I); % Convert color image to grayscale image Threshold=graythresh (I1); % calculates the threshold BW=IM2BW (I1, threshold) required to convert a grayscale image to a two-value image; % convert grayscale image to two value image subplot (2,2,2); Imshow (BW); title (' Binary image '); Dim=size (BW); Col=round (Dim (2)/2)-90; % calculates the starting point column coordinates row=find (BW (:, col), 1); % calculates the starting point line coordinates connectivity=8;num_points=180;contour=bwtraceboundary (Bw,[row,col], ' N ', connectivity,num_points); Extract boundary subplot (2,2,3); Imshow (I1); hold On;plot (Contour (:, 2), Contour (:, 1), ' G ', ' linewidth ', 2); title (' Boundary tracking image ');

The above code results:

TwelveHoughTransformations

Figure;i=imread (' input_image.jpg '), Roti=rgb2gray (I), subplot (2,2,1), Imshow (RotI), title (' Grayscale image '), Axis ([ 50,250,50,200]); grid on;axis on; Bw=edge (RotI, ' Prewitt '); subplot (2,2,2); Imshow (BW); title (' Prewitt operator edge detection image '); axis ([50,250,50,200]); Grid On;axis On [H,t,r]=hough (BW); subplot (2,2,3); Imshow (h,[], ' XData ', T, ' Ydata ', R, ' initialmagnification ', ' fit '); title (' Hough Transform graph '); Xlabel (' \theta '), Ylabel (' \rho '); axis on, Axis normal, hold on; P=houghpeaks (h,5, ' threshold ', ceil (0.3*max (H (:)))), X=t (P (:, 2)), Y=r (P (:, 1));p lot (x, y, ' s ', ' color ', ' white '); lines= Houghlines (bw,t,r,p, ' Fillgap ', 5, ' MinLength ', 7), subplot (2,2,4), Imshow (RotI), title (' Hough Transform image detection '), Axis ([ 50,250,50,200]); grid On;axis On;hold on;max_len=0;for k=1:length (lines) xy=[lines (k). Point1;lines (k). Point2];p Lot ( XY (:, 1), XY (:, 2), ' LineWidth ', 2, ' Color ', ' Green ';p lot (xy (), XY (+), ' x ', ' LineWidth ', 2, ' Color ', ' yellow ');p lot ( XY (2,1), XY (2,2), ' x ', ' LineWidth ', 2, ' Color ', ' Red '), Len=norm (lines (k). Point1-lines (k). Point2); if (Len>max_len) Max_len=len;xy_long=xy;endendplot (Xy_long(:, 1), Xy_long (:, 2), ' LineWidth ', 2, ' Color ', ' cyan '); 

The above code results:

13.Histogram threshold Value method

Figure;i=imread (' input_image.jpg '); I1=rgb2gray (I); subplot (2,2,1); Imshow (I1); title (' Grayscale image ') axis ([50,250,50,200]) ; grid on; % display grid line axis on; % display coordinate system [M,n]=size (I1); % measurement image size parameter Gp=zeros (1,256); % pre-created vector for K=0:255GP (k+1) =length (Find (I1==k))/(M*n) that holds the probability of grayscale occurrence; % calculates the probability of each level of grayscale appearing, depositing it into the corresponding position in GP Endsubplot (2,2,2), bar (0:255,GP, ' g ')% plotting histogram title (' Grayscale histogram ') xlabel (' grayscale value ') ylabel (' probability of occurrence ') i2= IM2BW (i,150/255); subplot (2,2,3), Imshow (I2); title (' Segmented image of threshold 150 ') axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I3=IM2BW (i,200/255); %subplot (2,2,4), Imshow (I3); title (' Segmented image of threshold 200 ') axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system

The above code results:

14.Automatic threshold Method:OtsuMethod

Clcclear allfigure;i=imread (' input_image.jpg '); subplot (1,2,1), Imshow (I); title (' Original image ') axis ([50,250,50,200]); grid On % display grid line axis on; % Display coordinate system Level=graythresh (I); % determines the grayscale threshold BW=IM2BW (I,level), subplot (1,2,2), Imshow (BW), title (' Otsu method threshold segmentation image ') axis ([50,250,50,200]), grid on; % display grid line axis on; % Display coordinate system

The above code results:

xvswellingOperation

Figure;i=imread (' input_image.jpg '); I1=rgb2gray (I); subplot (1,2,1); Imshow (I1); title (' Grayscale image ') axis ([50,250,50,200]) ; grid on; % display grid line axis on; % Display coordinate system Se=strel (' disk ', 1); % generated circular structural elements i2=imdilate (I1,SE); % expands the image with the generated structural elements subplot (1,2,2); Imshow (I2); title (' Expanded image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system

The above code results:

16.Corrosion Operation

Figure;i=imread (' input_image.jpg '); I1=rgb2gray (I); subplot (1,2,1); Imshow (I1); title (' Grayscale image ') axis ([50,250,50,200]) ; grid on; % display grid line axis on; % Display coordinate system Se=strel (' disk ', 1); % generated circular structural elements i2=imerode (I1,SE); % uses the resulting structural elements to corrode the image subplot (1,2,2); Imshow (I2); title (' Corroded image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system

The above code results:

17.opening and closing operations

Figure;i=imread (' input_image.jpg '); subplot (2,2,1), Imshow (I); title (' Original image '); axis ([50,250,50,200]); axis on; % Display coordinate system I1=rgb2gray (I); subplot (2,2,2), Imshow (I1); title (' Grayscale image '); axis ([50,250,50,200]); axis on; % Display coordinate system Se=strel (' disk ', 1); % using a circle with a radius of 1 as the structural element I2=imopen (I1,SE); % Open Operation I3=imclose (I1,SE); % closed operation subplot (2,2,3), Imshow (I2), Title (' Open op image '), axis ([50,250,50,200]); % display coordinate system subplot (2,2,4), Imshow (I3), title (' Closed Operation image '), axis ([50,250,50,200]); % Display coordinate system

The above code results:

18.opening and closing combination operations

Figure;i=imread (' input_image.jpg '); subplot (3,2,1), Imshow (I); title (' Original image '); axis ([50,250,50,200]); axis on; % Display coordinate system I1=rgb2gray (I); subplot (3,2,2), Imshow (I1); title (' Grayscale image '); axis ([50,250,50,200]); axis on; % Display coordinate system Se=strel (' disk ', 1); I2=imopen (I1,se); % Open Operation I3=imclose (I1,SE); % closed operation subplot (3,2,3), Imshow (I2), Title (' Open op image '), axis ([50,250,50,200]); % display coordinate system subplot (3,2,4), Imshow (I3), title (' Closed Operation image '), axis ([50,250,50,200]); % Display coordinate system Se=strel (' disk ', 1); I4=imopen (I1,se); I5=imclose (I4,se); subplot (3,2,5), Imshow (I5); % open-Close operation Image title (' Open-closed Operation image '); axis ([50,250,50,200]); axis on; % Display coordinate system i6=imclose (I1,SE); I7=imopen (I6,se); subplot (3,2,6), Imshow (I7); % closed-Open Operation Image title (' Closed-open operation Image '); axis ([50,250,50,200]); axis on; % Display coordinate system

The above code results:

NineteenMorphological Boundary Extraction

Figure;i=imread (' input_image.jpg '); subplot (2,3,1), Imshow (I); title (' Original image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I1=IM2BW (I); subplot (2,3,2), Imshow (I1); title (' Binary image '); axis ([50,250,50,200]); grid on; % display grid line axis on; % Display coordinate system I2=bwperim (I1); % gets the perimeter of the zone subplot (2,3,3), Imshow (I2); title (' Two-value image of the perimeter of the boundary '); axis ([50,250,50,200]); grid on;axis on; I3=bwmorph (I1, ' Skel ', 1); subplot (2,3,4), Imshow (I3); title (' 1-time skeleton extraction '); axis ([50,250,50,200]); axis on; I4=bwmorph (I1, ' Skel ', 2); subplot (2,3,5), Imshow (I4); title (' 2-time skeleton extraction '); axis ([50,250,50,200]); axis on;

The above code results:

Image segmentation based algorithm and implementation example