Thresholding based on smaller domains
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Hello all
I have an image (intl16) that I'm trying to detect defcts on them. First, what I tried to do was to get the mean/median value of the whole image and define some if condition to set any i,j to1 if they are above or lower the threshold (upper and lower). However, I have some images whic have regins with either higher or lower grayscle value. I wanted to try to have some small doimns (like 10by10 for the image 5120by5120) and get the mean value for the domins and theresholding for each domin separately. Is there anyone who has some experince to let me know how I can approach this problem?
any help will be really apprecited. I cannot share the imges, sorry.
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Answers (3)
KALYAN ACHARJYA
on 13 Feb 2021
Edited: KALYAN ACHARJYA
on 13 Feb 2021
You can try with any size, lets suppose you have image named as 'grayImage' (256x256)
grayImage=randi([0,255],[256,256]); %Random Image Data
% Find Mean
meanImage=mean2(grayImage)
% or Find Median
medImage=median(grayImage(:))
Define threshold, say "th"
th=150;
% Next suppose assign all those pixels greater than mean value of the image equal to zero (Black)
grayImage(grayImage>meanImage)=0;
Deals with threshold
grayImage(grayImage>th)=0;
More ways.......
Or Any logical condition as you wish to apply on image based on mean or median of the image.
Good Luck!
:)
Kalyan
2 Comments
KALYAN ACHARJYA
on 14 Feb 2021
I have shared the steps described in the description of the question. It would be easy to answer by looking at the images.
Jan
on 14 Feb 2021
Two solution:
X = randi([0,10], 12, 12); % arbitrary test data
n = 10; % Neighborhood
Y = conv2(x, ones(n, n) / (n * n), 'same');
mask = (X - Y) > Thresh;
X(mask) = Y(mask);
Or calculate the moving mean by:
Y1 = movmean(X, 10, 1);
Y = movmean(Y1, 10, 2);
% Replacing see above
This differs for the elements on the borders.
See also: medfilt2
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Image Analyst
on 16 Feb 2021
Did you even try the imbinarize() with the adaptive option like I suggested below?
Image Analyst
on 14 Feb 2021
imbinarize() has an 'adaptive' option try that. Otherwise try to use adapthisteq() to flatten the image so that you can use a global threshold. Attach a similar image (non-secret, non-proprietary) if you need more help.
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