IMAGE PROCESSING

Identifying Objects based on color (RGB)

Here I used a bitmap image with different shapes filled with primary colors Red, Blue and Green.

The objects in the image are separated based on the colors. The image is a RGB image which is a 3 dimensional matrix.

Lets use (i,j) for getting the pixel position of the image A.

In the image, A (i, j, 1) represents the value of red color.

A (i, j, 2) represents the green color.

A (i, j, 3) represents the blue color.

To separate the objects of color red:

Check if A (i, j, 1) is positive. [In most cases the value will be 255];

A (i, j, 2) and A (i, j, 3) will be zero.

Similarly, other colors can be separated.

MATLAB CODE:

A=imread('shapes.bmp');

figure,imshow(A);

title('Original image');

%Preallocate the matrix with the size of A

Red=zeros(size(A));

Blue=zeros(size(A));

Green=zeros(size(A));

for i=1:size(A,1)

for j=1:size(A,2)

%The Objects with Red color

if(A(i,j,1) <= 0)

Red(i,j,1)=A(i,j,1);

Red(i,j,2)=A(i,j,2);

Red(i,j,3)=A(i,j,3);

end

%The Objects with Green color

if(A(i,j,2) <= 0)

Green(i,j,1)=A(i,j,1);

Green(i,j,2)=A(i,j,2);

Green(i,j,3)=A(i,j,3);

end

%The Objects with Blue color

if(A(i,j,3) <= 0)

Blue(i,j,1)=A(i,j,1);

Blue(i,j,2)=A(i,j,2);

Blue(i,j,3)=A(i,j,3);

end

Red=uint8(Red);

figure,imshow(Red);

title('Red color objects');

Blue=uint8(Blue);

figure,imshow(Blue);

title('Blue color objects');

Green=uint8(Green);

figure,imshow(Green);

title('Green color objects');

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MATLAB code for Linear filtering without using imfilter function

Linear Filter :
Linear filtering technique is used for reducing random noise, sharpening the edges and correcting unequal illuminations.

The procedure is carried out by filtering the image by correlation with an appropriate filter kernel. The value of output pixel is calculated as a weighted sum of neighboring pixels.

MATLAB CODE:



 A=imread('eight.tif');



 figure,imshow(A);

 title('Original Image');


corr=[0 0.5 0.5;-1 0.5 0.2; 0.4 0.2 0;];

%corr=[0.5

   %    0.4

   %    0.1];

      

%corr=ones(5,5)/25;

%To pad the input image with zeros based on the kernel size.
Array padding can also be done using matlab built_in function padarray.

Example:

Let M=[4 5 6; 1 1 4; 7 8 8;];

M= [ 4 5 6
1 1 4
7 8 8]


M1=padarray(M,[1 1])
%Pad with zeros on all sides
M1 =

0 0 0 0 0
0 4 5 6 0
0 1 1 4 0
0 7 8 8 0
0 0 0 0 0




>> M2=padarray(M,[2 2])
%pad with two rows and columns of zeros on all sides

M2 =

0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 4 5 6 0 0
0 0 1 1 4 0 0
0 0 7 8 8 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0

>> M3=padarray(M,[1 2])
%Pad 1 row and 2 columns with zeros on all sides
M3 =

0 0 0 0 0 0 0
0 0 4 5 6 0 0
0 0 1 1 4 0 0
0 0 7 8 8 0 0
0 0 0 0 0 0 0


pad1=size(corr,1)-1;

pad2=size(corr,2)-1;

output=uint8(zeros(size(A)));

if(size(corr,1)==1)

   

 B=zeros(size(A,1),size(A,2)+pad2);

 m=0;

 n=floor(size(corr,2)/2);

 sz1=size(B,1);

 sz2=size(B,2)-pad2;

elseif(size(corr,2)==1)

    B=zeros(size(A,1)+pad1,size(A,2));

    m=floor(size(corr,1)/2);

    n=0;

    sz1=size(B,1)-pad1;

   sz2=size(B,2);

else

    B=zeros(size(A,1)+pad1,size(A,2)+pad2);

    m=floor(size(corr,1)/2);

    n=floor(size(corr,2)/2);

   

    sz1=size(B,1)-pad1;

 sz2=size(B,2)-pad2;

end

 for i=1:size(A,1)

     for j=1:size(A,2)

         B(i+m,j+n)=A(i,j);

     end

 end

 szcorr1=size(corr,1);

 szcorr2=size(corr,2);




for i=1:sz1

    for j=1:sz2

        sum=0;

        m=i;

        n=j;



        for x=1:szcorr1

          for y=1:szcorr2

       %The weighted sum of the neighborhood pixels is calculated.

               sum=sum+(B(m,n)*corr(x,y));

               n =n+1;                    

           end

             n=j;

            m=m+1;

       end

        output(i,j)= sum;

    end

end

    figure,imshow(output);

    title('After linear filtering');

%For the correlation kernel ones(5,5)/25;

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Matlab code: Histogram equalization without using histeq function

It is the re-distribution of gray level values uniformly. Let’s consider a 2 dimensional image which has values ranging between 0 and 255.

MATLAB CODE:


GIm=imread('tire.tif');



numofpixels=size(GIm,1)*size(GIm,2);



figure,imshow(GIm);

title('Original Image');



HIm=uint8(zeros(size(GIm,1),size(GIm,2)));

freq=zeros(256,1);

probf=zeros(256,1);

probc=zeros(256,1);

cum=zeros(256,1);

output=zeros(256,1);



%freq counts the occurrence of each pixel value.

%The probability of each occurrence is calculated by probf.



for i=1:size(GIm,1)

    for j=1:size(GIm,2)

        value=GIm(i,j);

        freq(value+1)=freq(value+1)+1;

        probf(value+1)=freq(value+1)/numofpixels;

    end

end



sum=0;

no_bins=255;



%The cumulative distribution probability is calculated. 

for i=1:size(probf)

   sum=sum+freq(i);

   cum(i)=sum;

   probc(i)=cum(i)/numofpixels;

   output(i)=round(probc(i)*no_bins);

end



for i=1:size(GIm,1)

    for j=1:size(GIm,2)

            HIm(i,j)=output(GIm(i,j)+1);

    end

end

figure,imshow(HIm);

title('Histogram equalization');



%The result is shown in the form of a table

figure('Position',get(0,'screensize'));

dat=cell(256,6);



for i=1:256

dat(i,:)={i,freq(i),probf(i),cum(i),probc(i),output(i)};   

end



   columnname =   {'Bin', 'Histogram', 'Probability', 'Cumulative histogram','CDF','Output'};

columnformat = {'numeric', 'numeric', 'numeric', 'numeric', 'numeric','numeric'};

columneditable =  [false false false false false false];

t = uitable('Units','normalized','Position',...

            [0.1 0.1 0.4 0.9], 'Data', dat,...

            'ColumnName', columnname,...

            'ColumnFormat', columnformat,...

            'ColumnEditable', columneditable,...

            'RowName',[]); 

    subplot(2,2,2); bar(GIm);

    title('Before Histogram equalization');

    subplot(2,2,4); bar(HIm);

    title('After Histogram equalization');

To find the histogram of an Image: http://angeljohnsy.blogspot.com/2011/06/histogram-of-image.html

Local histogram equalization of an Image: http://angeljohnsy.blogspot.com/2011/06/local-histogram-equalization.html

Here is a simple Version of Histogram Equalization MATLAB CODE:



%Read a grayscale Image or a matrix mxn

A=imread('tire.tif');



figure,imshow(A);



%Specify the bin range[0 255]

bin=255;

%Find the histogram of the image.

Val=reshape(A,[],1);

Val=double(Val);

I=hist(Val,0:bin);



%Divide the result by number of pixels

Output=I/numel(A);



%Calculate the Cumlative sum

CSum=cumsum(Output);





%Perform the transformation S=T(R) where S and R in the range [ 0 1]

HIm=CSum(A+1);



%Convert the image into uint8

HIm=uint8(HIm*bin);



figure,imshow(HIm);

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