Applying Blob Detection Technique to the Original Image 2

clc;
clear all;
originalImage = imread('coins.png');
subplot(3, 3, 1);
imshow(originalImage);
% Maximize the figure window.
set(gcf, 'Position', get(0, 'ScreenSize'));
[pixelCount grayLevels] = imhist(originalImage);
subplot(3, 3, 2);
bar(pixelCount); title('Histogram of original image');
xlim([0 grayLevels(end)]); % Scale x axis manually.
  thresholdValue = 100;
  binaryImage = originalImage > thresholdValue;



binaryImage = imfill(binaryImage, 'holes');
% Display the binary image.
subplot(3, 3, 3); imagesc(binaryImage); colormap(gray(256)); title('Binary Image, obtained by thresholding'); axis square;
labeledImage = bwlabel(binaryImage, 8);    
% Label each blob so we can make measurements of it
coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle');
% pseudo random color labels
subplot(3, 3, 4); imshow(labeledImage, []);
title('Labeled Image, from bwlabel()');
axis square; subplot(3, 3, 5); imagesc(coloredLabels);
axis square;
caption = sprintf('Pseudo colored labels, from label2rgb().\nBlobs are numbered from top to bottom, then from left to right.');
title(caption);
blobMeasurements = regionprops(labeledImage, originalImage, 'all');  
numberOfBlobs = size(blobMeasurements, 1);
subplot(3, 3, 6); imagesc(originalImage);
title('Outlines, from bwboundaries()'); axis square; hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries);

for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k};
plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);
end
hold off;

Applying Blob Detection Technique to the Original Image

clc;

clear all;

originalImage = imread('coins.png');

subplot(3, 3, 1);

imshow(originalImage);

% Maximize the figure window.

set(gcf, 'Position', get(0, 'ScreenSize'));

[pixelCount grayLevels] = imhist(originalImage);

subplot(3, 3, 2);

bar(pixelCount); title('Histogram of original image');

xlim([0 grayLevels(end)]); % Scale x axis manually.

thresholdValue = 100;

binaryImage = originalImage > thresholdValue;

binaryImage = imfill(binaryImage, 'holes');

% Display the binary image.

subplot(3, 3, 3); imagesc(binaryImage); colormap(gray(256)); title('Binary Image, obtained by thresholding'); axis square;

labeledImage = bwlabel(binaryImage, 8); 

coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle');

subplot(3, 3, 4);              imshow(labeledImage, []);          

title('Labeled Image, from bwlabel()');

axis square;      subplot(3, 3, 5);                                

imagesc(coloredLabels);

axis square;

caption = sprintf('Pseudo colored labels, from label2rgb().\nBlobs are numbered from top to bottom, then from left to right.');

title(caption);

blobMeasurements = regionprops(labeledImage, originalImage, 'all');  

numberOfBlobs = size(blobMeasurements, 1);

subplot(3, 3, 6); imagesc(originalImage);

title('Outlines, from bwboundaries()'); axis square;       hold on;

boundaries = bwboundaries(binaryImage);      

numberOfBoundaries = size(boundaries);

for k = 1 : numberOfBoundaries

              thisBoundary = boundaries{k};

              plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);

end

hold off;

Applying Corner Detection Technique to the Original Image

I = checkerboard(50,2,2);

C = corner(I);

imshow(I);

hold on

plot(C(:,1), C(:,2), 'r*');

Applying Watershed Segmentation to the Original Image

I = imread('image.jpg');

subplot(1,2,1); imshow(I); title('Original Image');

se = strel('diamond',7);

Itop = imtophat(I,se);

Ibot = imbothat(I,se);

Ienhance = imsubtract(imadd(Itop,30), Ibot);

Iec = imcomplement(Ienhance);

Iemin = imextendedmin(Iec,22);

Iimpose = imimposemin(Iec,Iemin);

Iwat = watershed(Iimpose);

Iseg = label2rgb(Iwat);

subplot(1,2,2); imshow(Iseg); title('Watershed Segmented Image');

Applying Dilation and Erosion to the Original Image

Lenna = imread('lenna2.jpg');

SE3 = strel('square',3);

SE5 = strel('square',5);                                   

SE7 = strel('square',7);

ErodeLenna = imerode(Lenna,SE3);

subplot(2,3,1); imshow(ErodeLenna);  ylabel('Eroded Image'); title('5x5');

ErodeLenna = imerode(Lenna,SE5);

subplot(2,3,2); imshow(ErodeLenna); title('5x5');

ErodeLenna = imerode(Lenna,SE7);

subplot(2,3,3); imshow(ErodeLenna); title('7x7');

Converting Grayscale Image into Binary Image (Black-White)

Lenna = imread('lenna.png');

GrayLenna = rgb2gray(Lenna);

bwLenna = im2bw(GrayLenna);

subplot(1,2,1); imshow(bwLenna);                          

subplot(1,2,2); imhist(bwLenna)

Creating a Histogram of an Original Image

Lenna = imread('lenna.png');                                                     

GrayLenna = rgb2gray(Lenna);

imhist(GrayLenna)

Creating the Negative of an Original Image

negLenna = double(Lenna);

negLennaScale = 1.0/max(negLenna(:));

negLenna = 1 - negLenna*negLennaScale;

figure;

imshow(negLenna)

Implementing Edge Detection Techniques to an Original Image

Lenna = imread('Lenna2.jpg');

figure;   SobLenna = edge(Lenna, 'sobel', 0.055, 'horizontal');

subplot(1,3,1);  imshow(SobLenna); xlabel('Horizontal');

SobLenna = edge(Lenna, 'sobel', 0.055, 'vertical'); 

subplot(1,3,2);  imshow(SobLenna); xlabel('Vertical'); title('Sobel Edge Detection');

SobLenna = edge(Lenna, 'sobel', 0.055, 'both');

subplot(1,3,3);  imshow(SobLenna); xlabel('Both');

figure;   PreLenna = edge(Lenna, 'prewitt', 0.055, 'horizontal');

subplot(1,3,1);  imshow(PreLenna); xlabel('Horizontal');

PreLenna = edge(Lenna, 'prewitt', 0.055, 'vertical'); 

subplot(1,3,2);  imshow(PreLenna); xlabel('Vertical'); title('Prewitt Edge Detection');

PreLenna = edge(Lenna, 'prewitt', 0.055, 'both');

subplot(1,3,3);  imshow(PreLenna); xlabel('Both');

figure;   RobLenna = edge(Lenna, 'roberts');

subplot(1,3,1);  imshow(RobLenna); xlabel('With Default Threshold');

RobLenna = edge(Lenna, 'roberts', 0.055);

subplot(1,3,2);  imshow(RobLenna); xlabel('Threshold = 0.055'); title('Roberts Edge Detection');

RobLenna = edge(Lenna, 'roberts', 0.1);

subplot(1,3,3);  imshow(RobLenna); xlabel('Threshold = 0.1');

figure;   LogLenna = edge(Lenna, 'log');

subplot(1,3,1);  imshow(LogLenna); xlabel('With Default Threshold & Sigma');

LogLenna = edge(Lenna, 'log', 0.0055);

subplot(1,3,2);  imshow(LogLenna); xlabel('Threshold = 0.0055 & Default Sigma');

title('Laplacian of Gaussian Edge Detection');

LogLenna = edge(Lenna, 'log', 0.0055, 1.5);

subplot(1,3,3);  imshow(LogLenna); xlabel('Threshold = 0.0055 & Sigma = 1.5');

figure;   CanLenna = edge(Lenna, 'canny');

subplot(1,3,1);  imshow(CanLenna); xlabel('With Default Threshold & Sigma');

CanLenna = edge(Lenna, 'canny', 0.155);

subplot(1,3,2);  imshow(CanLenna); xlabel('Threshold = 0.155 & Default Sigma');

title('Canny Edge Detection');

CanLenna = edge(Lenna, 'canny', 0.155, 1.25);

subplot(1,3,3);  imshow(CanLenna); xlabel('Threshold = 0.155 & Sigma = 1.25');

Removing Noise from a Noisy Corrupted Image using various Filtering Techniques

Lenna = imread('lenna2.jpg');

NoiseLenna = imnoise(Lenna,'salt & pepper',0.02);

GF1 = fspecial('gaussian',[3 3],0.5);

GF2 = fspecial('gaussian',[5 5],0.5);

GF3 = fspecial('gaussian',[7 7],0.5);

AV1 = [1 1 1;

            1 1 1;

            1 1 1]/9;

AV2 = [1 1 1 1 1;

            1 1 1 1 1;

            1 1 1 1 1;

            1 1 1 1 1;      

            1 1 1 1 1]/25;

AV3 = [1 1 1 1 1 1 1;

            1 1 1 1 1 1 1;

            1 1 1 1 1 1 1;

            1 1 1 1 1 1 1;

            1 1 1 1 1 1 1;

            1 1 1 1 1 1 1;      

            1 1 1 1 1 1 1]/49;

  

GaussLenna = imfilter(NoiseLenna,GF1);

subplot(3,4,1);  imshow(GaussLenna);  ylabel('3x3'); title('Gaussian Filter');

GaussLenna = imfilter(NoiseLenna,GF2);

subplot(3,4,5);  imshow(GaussLenna);  ylabel('5x5');

GaussLenna = imfilter(NoiseLenna,GF3);

subplot(3,4,9);  imshow(GaussLenna);  ylabel('7x7');

AvgLenna = imfilter(NoiseLenna,AV1); 

subplot(3,4,2);  imshow(AvgLenna);  title('Averaging Filter');

AvgLenna = imfilter(NoiseLenna,AV2);

subplot(3,4,6);  imshow(AvgLenna);

AvgLenna = imfilter(NoiseLenna,AV3);

subplot(3,4,10);  imshow(AvgLenna);

MedLenna = medfilt2(NoiseLenna,[3 3]); 

subplot(3,4,3);  imshow(MedLenna);  title('Median Filter');

MedLenna = medfilt2(NoiseLenna,[5 5]);

subplot(3,4,7);  imshow(MedLenna);

MedLenna = medfilt2(NoiseLenna,[7 7]);

subplot(3,4,11);  imshow(MedLenna);

OrdLenna = ordfilt2(NoiseLenna,5,ones(3,3)); 

subplot(3,4,4);  imshow(OrdLenna);  title('Ordered Filter');

OrdLenna = ordfilt2(NoiseLenna,5,ones(5,5));

subplot(3,4,8);  imshow(OrdLenna);

OrdLenna = ordfilt2(NoiseLenna,5,ones(7,7));

subplot(3,4,12);  imshow(OrdLenna);

Adding Noise to an Original Image

 Lenna = imread('Lenna2.jpg');

 SPNoiseLenna = imnoise(Lenna,'salt & pepper',0.07);

 GNoiseLenna = imnoise(Lenna,'gaussian',0.01,0.005);

 PNoiseLenna = imnoise(Lenna,'poisson');

 SNoiseLenna = imnoise(Lenna,'speckle',0.06);

 subplot(2,2,1); imshow(SPNoiseLenna);  title('Salt & Pepper Noise');

 subplot(2,2,2); imshow(GNoiseLenna);   title('Gaussian Noise');

 subplot(2,2,3); imshow(PNoiseLenna);   title('Poisson Noise');

 subplot(2,2,4); imshow(SNoiseLenna);   title('Speckle Noise');

Finding Out the Fourier and Inverse Fourier Transform of an Image

A = [ 0 0 0 0 0 0 0 0;

         0 0 0 0 0 0 0 0;

         0 0 0 1 1 0 0 0;

         0 0 0 1 1 0 0 0;

         0 0 0 1 1 0 0 0;

         0 0 0 1 1 0 0 0;

         0 0 0 0 0 0 0 0;

         0 0 0 0 0 0 0 0]

iptsetpref('ImshowTruesize','manual') figure;

subplot(3,2,1); imshow(A); title('Original Image');

subplot(3,2,2); mesh(A); title('Mesh Plot of Original Image');

B = fft2(A);

subplot(3,2,3); imshow(B); title('2-D FFT of Original Image');

subplot(3,2,4); mesh(B); title('Mesh Plot of 2-D FFT');

C = ifft2(B);

subplot(3,2,5); imshow(C); title('Inv 2-D FFT of Above Image');

subplot(3,2,6); mesh(C); title('Mesh Plot of Inv 2-D FFT');

Converting an Image into Grayscale

Lenna = imread('lenna.png');

GrayLenna = rgb2gray(Lenna);                                                  

imshow(GrayLenna)

Loading and Displaying an Image

Lenna = imread('lenna.png');

imshow(Lenna)