How to compute the mean of two disjoint region ?
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Makrim
el 1 de Mayo de 2015
Comentada: Image Analyst
el 2 de Mayo de 2015
Suppose I have to fragment of an image J : J_out_1 et J_out_2.
J_out_1 = J(1:h,startj:i);
J_out_2 = J(1:h,k:endj);
I would like to compute the mean of the union of those two regions , is it possible ?
m_out = mean2(J_out_1 union J_out_2);
Thank you in advance
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Guillaume
el 1 de Mayo de 2015
Editada: Guillaume
el 1 de Mayo de 2015
m_out = mean([J_out_1(:); J_out_2(:)])
would be one way to do it assuming the image has only one colour channel. If they are RGB images:
m_out = mean([reshape(J_out_1, 1, [], 3), reshape(J_out_2, 1, [], 3)])
Note that if the two regions are the same size, you could just concatenate them without any reshaping (by colon or reshape).
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Image Analyst
el 1 de Mayo de 2015
Why not just take the weighted mean of the two?
numerator = numel(J_out_1) * mean2(J_out_1) + numel(J_out_2) * mean2(J_out_2)
denominator = numel(J_out_1) + numel(J_out_2)
m_out = numerator / denominator
If you want, you could make a binary image and use that as a mask to extract all the pixels in just the two regions:
binaryImage = false(size(J));
binaryImage(1:h,startj:i) = true;
binaryImage(1:h,k:endj) = true;
m_out = mean(J(binaryImage))
2 comentarios
Image Analyst
el 2 de Mayo de 2015
You're welcome. Those ways will also work even if the two subimages don't have the same number of rows. So "h" could be different for each image and they would still work.
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