How to compute the mean of two disjoint region ?

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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

Accepted Answer

Guillaume
Guillaume on 1 May 2015
Edited: Guillaume on 1 May 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).
  1 Comment
Makrim
Makrim on 2 May 2015
excellent, that's what I am looking for.finally I have done it as follow :
m_out = mean2([J_out_1 , J_out_2])

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More Answers (1)

Image Analyst
Image Analyst on 1 May 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 Comments
Makrim
Makrim on 2 May 2015
that's another way, Yes , thank you. by the way the idea of using Logical is also brilliant.
Image Analyst
Image Analyst on 2 May 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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