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Can I represent an image in a binary tree format?

srikanth Appala さんによって質問されました 2013 年 3 月 19 日
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  1 件のコメント

Alessandro 2013 年 3 月 19 日
can t understand what you wannt

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2 件の回答

回答者: Image Analyst
2013 年 3 月 19 日

See qtdecomp() in the Image Processing Toolbox.

  6 件のコメント

Image Analyst
2013 年 3 月 20 日
No one knows what you even want. Please give us a small image, say 16 by 16, and show us what you want the "output" to be. Why can't you just use the array? Why do you need it in a "binary tree" format instead? How do you define a "binary tree"?
sir, my project is "Binary partitioning tree as an efficient representation for image segmentation, information retrieval and image processing".so binary partitioning with image regions is important ro to us.to devide image into regions "region merging algorithm" is used,bt i dont knw how construct tree.help me
Image Analyst
2013 年 3 月 21 日
I've used qtdecomp only briefly once and that was to just understand how it works. I never need to do that. It doesn't return some information that I needed and when I called the Mathworks they weren't too clear on how it worked either. Anyway, I don't use it. Not sure why you think you need to do this or why you chose that project subject. Can you explain why? Better yet, start your own discussion, rather than intertwine your discussion with Allesandro's.

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回答者: Alessandro 2013 年 3 月 21 日
編集済み: Alessandro 2013 年 3 月 21 日

from wikipedia I read the following about image segmentation:
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze
You need a tree and the "superpixels" values of the tree. I just wannted to understand the sparse objects from matlab so I tryed the qtdecomp function:
%define some grayscale image
I = uint8([1 1 1 1 2 3 6 6;...
1 1 2 1 4 5 6 8;...
1 1 1 1 7 7 7 7;...
1 1 1 1 6 6 5 5;...
20 22 20 22 1 2 3 4;...
20 22 22 20 5 4 7 8;...
20 22 20 20 9 12 40 12;...
20 22 20 20 13 14 15 16]);
%Get where there is information
S = qtdecomp(I,.05);
%Get the information using the simply mean value
erg = sparse(0);
blocks = unique(nonzeros(S));
for blocksize = blocks'
[y x] = find(S==blocksize);
for i=1:length(x)
erg(x(i),y(i)) = mean2(I(y(i):y(i)+blocksize-1,x(i):x(i)+blocksize-1));
end
end
rebuildimage = zeros(size(S));
%Rebuild the image from the mean values in the block
for blocksize = blocks'
[y x] = find(S==blocksize);
for i=1:length(x)
rebuildimage(y(i):y(i)+blocksize-1,x(i):x(i)+blocksize-1) = nonzeros(erg(x(i),y(i)))
end
end
disp(rebuildimage)
So now you can see rebuildimage looks like I. In the matlab sparse arrays S and erg you have the "super pixels" information.

  4 件のコメント

Alessandro 2013 年 3 月 21 日
I would say they could be interesting for wavelet decomposition. But they feels a bit to theoritical for me :)
Mohammed
2013 年 4 月 4 日
It is useful for things like topology representation of the segmentation maps. It saves a lot of time for searching algorithms instead of doing linear searching, they use them as a probabilistic framework of searching that can reduce the time by a huge factor (from week to 10 mins of runtime)... I would suggest you to read about huffman coding and binay trees for more understanding about the tree representation!
Image Analyst
2013 年 4 月 4 日
What would you be searching for?

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