Stitching sub images to reconstruct full image
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답변: KHOR WEI KOK 2016년 9월 2일
I have subdivided an image into 4x4 tiles and produced a subimage and obtained a binary imaged. I add each of these binary images to a binary stack using a loop.
After the loop, how do I reconstruct/stitch the sub binary images back to the full image size, so I can create a binary image representing the while raw image so to use as a mask:
My binary sub images are expressed as :
BI(:,:,i) %where i is 1:16 as I'm using 4x4 tiles
This is my approach that isn't working:
%Now combine binary images so to create regions to act as mask on original
size(BI) %Confirm there are 16 planes of images in the binarystack
Binary=; %Create empty Binary Image that will hold reconstructed sub images
Mohammad Abouali 2015년 1월 14일
편집: Mohammad Abouali 2015년 1월 14일
Again, why don't you use blockproc()? :D
what you are asking, i.e. stitching the sub images into one big one, can be done in a single command using blockproc() as follows:
Binary=blockproc( reshape(1:16,4,4)', [1,1], @(x) BI(:,:,x.data) );
where Binary contains all the sub images placed in the order you show in the figure. 16 sub images are not much but if there were more you can even do this in parallel as easily as:
Binary =blockproc( reshape(1:16,4,4)', [1,1], @(x) BI(:,:,x.data), 'UseParallel',true);
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Iain 2015년 1월 13일
depending how you've done it....
BInew = permute(BI,[1 3 2]);
BInew = reshape(BInew,16*size(BI,1),);
ought to work.
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Why bother saying this:
if you're just going to say this
three lines later? Not only that, but you switched rows and columns. The first index should be rows, not columns as you have it, and the second index should be columns, not rows as you have it.
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The first index should be rows, not columns as you have it, and the second index should be columns, not rows as you have it.
Alessandro Masullo 2015년 1월 13일
What about using mat2cell and cell2mat? It should be much easier.
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Image Analyst 2015년 1월 14일
OK but you don't need to split apart your image to do that. You can get a better local thresholding using adapthisteq() to flatten your image and then use a global threshold, or use blockproc like Mohammad suggested. adapthisteq is like splitting your image apart into tons of tiles that are only a pixel apart and will be much better and more accurate than splitting your image into 4 tiles and then computing the threshold for the whole tile. You should really look into these methods.
KHOR WEI KOK 2016년 9월 2일
Hi, I would like to ask, how you manage to show the line segmentation and numbering on your image axes?
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