Image Processing Lab Report

SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Digital Image Processing Lab 9 Report

Ahmed Abbas Muhammad Aitzaz Hussain

2009-NUST-BE-EL-09 2009-NUST-BE-EL-51

Digital Image Processing

Lab-09 Report

Run Length Smoothing Algorithm (RLSA) based Text Segmentation.

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Open the image files provided on LMS.

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Read into Matlab, remove any noise, and convert to binary.

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Apply Horizontal RLSA.

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Apply Vertical RLSA.

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Combine vertical and horizontal outputs to segment the text regions.

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Now, change the parameters to extract the words from the title of the paper

only (the two largest font lines on the first image) .

Code: image=rgb2gray(imread('Page (2).tif')); image=imcomplement(im2bw(image,0.5)); %imshow(image); [m,n]=size(image); hor_thresh=20; zeros_count=0; one_flag=0; hor_image=image; for i=1:m for j=1:n if(image(i,j)==1) if(one_flag==1) if(zeros_count<=hor_thresh) hor_image(i,j-zeros_count:j-1)=1; else one_flag=0; end zeros_count=0; end one_flag=1; else if(one_flag==1) zeros_count=zeros_count+1; Ahmed Abbas

Muhammad Aitzaz Hussain


Lab-09 Report

end end end end figure(1); imshow(hor_image); title('Horizontal RLSA'); zeros_count=0; one_flag=0; vert_thresh=30; vert_image=image; for j=1:n for i=1:m if(image(i,j)==1) if(one_flag==1) if(zeros_count<=vert_thresh) vert_image(i-zeros_count:i-1,j)=1; else one_flag=0; end zeros_count=0; end one_flag=1; else if(one_flag==1) zeros_count=zeros_count+1; end end end end figure(2); imshow(vert_image); title('Vertical RLSA'); figure(3); rlsa=bitand(hor_image,vert_image); imshow(rlsa); title('RLSA');

Ahmed Abbas



Ahmed Abbas

Lab-09 Report



Ahmed Abbas

Lab-09 Report



Lab-09 Report

Lab 09 b K-mean and C-mean.

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Read Image 1 into Matlab, remove any noise, and convert to binary, and invert colors.

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Apply K mean clustering for the four objects.

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Then apply C mean clustering.

Set any parameter that is not mentioned here on your own. Write the number of iterations for the algorithms to converge.

K-Means Clustering Number of Clusters is a variable and we can set any value of number of clusters (K).

Main Code: image=imcomplement((im2bw(imread('2.bmp')))); clusters=4;

% <- Variable

figure(1); [pixels_x,pixels_y]=find(image==1); [m]=length(pixels_x); centroid_indices=randi([1 m], clusters,1); centroid_x=pixels_x(centroid_indices); centroid_y=pixels_y(centroid_indices); all_centroids_x=[]; all_centroids_y=[]; dist=zeros(length(pixels_x),clusters); imshow(imcomplement(image)); hold on; while(1) [new_centroid_x,new_centroid_y]=kmeans_calc( centroid_x,centroid_y,pixels_x,pixels_y,dist,clusters); Ahmed Abbas



Lab-09 Report

all_centroids_x=[all_centroids_x centroid_x]; all_centroids_y=[all_centroids_y centroid_y]; if(isequal(new_centroid_x,centroid_x) && isequal(new_centroid_y,centroid_y)) break; end centroid_x=new_centroid_x; centroid_y=new_centroid_y; end for i=1:size(all_centroids_x,2)-1 for j=1:clusters plot([all_centroids_y(j,i) all_centroids_y(j,i+1)],[all_centroids_x(j,i) all_centroids_x(j,i+1)],'-k','linewidth',1.5); plot(all_centroids_y(j,:),all_centroids_x(j,:), 'x','linewid th',3,'color',[rand rand rand]); end end plot(all_centroids_y(:,1),all_centroids_x(:,1), 'o','Linewid th',3,'color',[1 1 1]);

Function Code: function [ new_centroid_x,new_centroid_y] = kmeans_calc( centroid_x,centroid_y,pixels_x,pixels_y,dist,clusters ) new_centroid_x=zeros(clusters,1); new_centroid_y=zeros(clusters,1); for i=1:clusters dist(:,i)=(pixels_x-centroid_x(i)).^2 + (pixels_y centroid_y(i)).^2; end [~,centroid_assign]=min(dist,[],2); % index, value of min dist for j=1:clusters indices=find(centroid_assign==j); new_centroid_x(j)=mean(pixels_x(indices)); new_centroid_y(j)=mean(pixels_y(indices)); plot(pixels_y(indices),pixels_x(indices), '.','color',[rand rand rand]); end end Ahmed Abbas



Lab-09 Report

Results:

Ahmed Abbas



Lab-09 Report

Note:  

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The points labeled as ‘o’ are the initial random values of clusters. The points labeled as ‘x’ are the calculated values of clusters during all the iterations. The lines indicate the path travelled by the centroids.

C-Means Clustering Number of Clusters is a variable and we can set any value of number of clusters (K).

Main Code: image=imcomplement((im2bw(imread('2.bmp')))); clc close all clusters=4; epsilon=0.1; figure(1); [pixels_row,pixels_col]=find(image==1); [m]=length(pixels_row); centroid_indices=randi([1 m], clusters,1); centroid_row=pixels_row(centroid_indices); centroid_col=pixels_col(centroid_indices); old_crow=centroid_row; old_ccol=centroid_col; dist=zeros(m,clusters); imshow((image)); hold on;

Ahmed Abbas



Lab-09 Report

while(1) [new_centroid_row,new_centroid_col]=cmeans_calc( centroid_row,centroid_col,pixels_row,pixels_col,dist,cl usters); plot(centroid_col,centroid_row,'*','color',[.3 .7 .2]); if(max(abs(centroid_row-new_centroid_row))
Function Code: function [new_centroid_row,new_centroid_col]=cmeans_calc( centroid_row,centroid_col,pixels_row,pixels_col,dist,cl usters) for i=1:clusters dist(:,i)=sqrt((pixels_row-centroid_row(i)).^2 + (pixels_col -centroid_col(i)).^2); end sum_dist=repmat(sum(dist,2),1,clusters); prob_dist=dist./sum_dist; weights_dist=(1-(prob_dist)); weights_dist=weights_dist./repmat(sum(weights_dist),len gth(pixels_row),1); % division to ensure sum(weights)=1 pixels_row=repmat(pixels_row,1,clusters); pixels_col=repmat(pixels_col,1,clusters); new_centroid_row=((sum(pixels_row.*weights_dist)))'; new_centroid_col=((sum(pixels_col.*weights_dist)))'; end

Ahmed Abbas



Lab-09 Report

Results:

Analysis: The fuzzy c-mean algorithm taught in class causes the centroids to converge to same point that is, the mean of all the foreground pixels of the image, irrespective of the initial positions of the centroids. As evident, we are not getting satisfactory results. The actual fuzzy c-mean algorithm is somewhat more complex than the way we learned and implemented. The code presented here is strictly built upon the algorithm from the lecture and was debugged through calculations by pen and paper many a times for checking presence of any error.

Ahmed Abbas


Image Processing Lab Report

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