How do I apply SVD (Singular Value Decomposition) to an image?
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The syntax given for singular value decomposition is svd(x).
I tried it with my image, but it didn't work. Can you tell me how to work with svd for images please?
2 个评论
David Young
2011-3-2
Please could you say what the error message was, and also show any other parts of your code that might be relevant.
Andreas Goser
2011-3-2
While I agree with David on the need for specifics, my crystal ball tells my this is about data types and will craft an answer for that...
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更多回答(5 个)
meenakshi
2011-9-6
HELLO GOSER
i=imread('pout.tif');
i=im2double(i)
[u s v]=svd(i);
you can try like this.
k.meenakshi
1 个评论
Walter Roberson
2011-9-6
That would not have any more success than svd(double(I)) if I is a truecolor (3D) image. Remember, images can be stored as pseudocolor (2D arrays in which the values indicate which index to use out of a color map), or as truecolor (3D arrays in which the values directly indicate the color information for each pixel without any map.) The problem is that svd() of a pseudocolor image is not meaningful, and svd() of a 3D array is not allowed. The only choice available to get anything useful out is to convert the image to grayscale and svd() the grayscale image.
slama najla
2012-4-21
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Hello, can some body help me with the code of SVD decomposition in 3d medical data in matlab please.
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Walter Roberson
2012-4-21
No. SVD is only for 2D.
You could take the svd() of each plane.
slama najla
2012-4-28
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But many approaches use it us decomposition for 3d data in watermarking,this is why i reask this question.thanks
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Walter Roberson
2012-4-28
SVD is *defined* in terms of rectangular matrices. There is no method to apply SVD to a 3D matrix. I looked at some of the articles about color image watermarking using svd, and of the ones I could access, not one of them attempted to apply SVD to a 3D matrix.
Neelam Yadav
2020-9-18
Hello, can anyone help me how I get different image for U,S,V in svd
Ayesha Iftikhar
2018-9-19
0 个投票
Hello can any one help me how to use SVD for feature extraction
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