How can i calculate vertical histogram. I took a code from somewhere which works for Horizontal processing but how i change it to work for vertical processing. The code is below

img=rgb2gray(imread('n3.jpg'));
[rows,cols] = size(img);
img2 = zeros(rows,cols);

%Calculated the gradients
for i =1:1:rows
    for j =1:1:cols-1
        img2(i,j) = abs(img(i,j+1) - img(i,j));
    end
end

% calculated the mean of gradients to determine which pixels hold the value
% that is above the average difference as the paper says `Then the average
% gradient variance is calculated and compared with each other. The bigger
% intensity variations provide the rough estimation of license plate region.`

M = mean2(img2);
for i =1:1:rows
    for j =1:1:cols-1
        if(abs(img(i,j+1) - img(i,j))<M)
            img2(i,j)=0;
        else
            img2(i,j)=100;
        end
    end
end

% Applied average filter to reduce the noise
img2 = filter2(fspecial('average',2),img2)/255;

% Output is shown
figure,imshow(img2);title('Gradient');

[Ayaz Wazir]

Dear Walter Roberson i used your proposed method i mean "Transpose of image'" for vertical image processing but couldn't get the required result. my reqiured result is to extract the object after vertical processing as like the third object in sumit gupta image which i uploaded.

[Ayaz Wazir]

Answer 1

[Walter Roberson]

Easiest way: transpose the image before passing it to the existing routine.

[Walter Roberson]

img=rgb2gray(imread('n3.jpg'));
[rows,cols] = size(img);
img2 = zeros(rows,cols);

%Calculated the gradients
for i =1:1:rows-1
    for j =1:1:cols
        img2(i,j) = abs(img(i+1,j) - img(i,j));
    end
end

% calculated the mean of gradients to determine which pixels hold the value
% that is above the average difference as the paper says `Then the average
% gradient variance is calculated and compared with each other. The bigger
% intensity variations provide the rough estimation of license plate region.`

M = mean2(img2);
for i =1:1:rows-1
    for j =1:1:cols
        if(im2(i,j)<M)
            img2(i,j)=0;
        else
            img2(i,j)=100;
        end
    end
end

% Applied average filter to reduce the noise
img2 = filter2(fspecial('average',2),img2)/255;

% Output is shown
figure,imshow(img2);title('Gradient');