Evaluation of image segmentation without using a model
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Hello guys, I've been trying to see who is better at image segmentation is it using only the green channel mask the whole image mask
here is my code for segmentation using green channel
I=imread(image);
g=I(:, :, 2);
%binarizing images
gg=imbinarize(rescale(g));
SE=strel('disk',5);
%green
gg=imopen(gg,SE);
gg=imdilate(gg,SE);
gg = bwareafilt(gg,1);
maskedRgbImageg= imoverlay(I,gg,'k');
my segmentation code using the whole image
I=imread(image);
gray=rgb2gray(I);
%binarizing images
graym=imbinarize(rescale(gray));
SE=strel('disk',5);
graym=imopen(graym,SE);
graym=imdilate(graym,SE);
graymmaskedRgbImageg= imoverlay(I,graym,'k');
and now I don't know how to evalute each one segmentation using the metrics, is there is any methode that can show me who segments better. thank you in advance
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DGM
2022년 5월 12일
편집: DGM
2022년 5월 12일
That's entirely dependent on the image, the content, and the intent. It's not clear what those are, and it's not clear what "the metrics" are either.
Just grabbing an offhand color image,
I get this:
I = imread('jagblobs.png');
g = I(:, :, 2);
%binarizing images
gg = imbinarize(rescale(g));
SE = strel('disk',5);
gg = imopen(gg,SE);
gg = imdilate(gg,SE);
gg = bwareafilt(gg,1);
maskedRgbImageg = imoverlay(I,gg,'k');
imshow(maskedRgbImageg)
... and this
I = imread('jagblobs.png');
gray = rgb2gray(I);
%binarizing images
graym = imbinarize(rescale(gray));
SE = strel('disk',5);
graym = imopen(graym,SE);
graym = imdilate(graym,SE);
graymmaskedRgbImageg = imoverlay(I,graym,'k');
imshow(graymmaskedRgbImageg)
Neither of which really help to make the intent clear (the approximate removal of the largest object, maybe?).
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Image Analyst
2022년 5월 13일
I would use whatever image gave you the most contrast in what you want to find. Usually a single color channel will give you the most contrast if you're looking for something that is a single color. If you want to find multiple colors simultaneously then useing rgb2gray would probably be best. You might even be able to use PCA to get more contrast. PCA Demos attached.
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