Enclosing Boundary - for blobs
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Hi all
Is it possible to get the boundary central more dense region - ignoring the blobs on the side
6 commentaires
DGM
le 3 Juil 2021
You might want to describe the method you're currently using.
Image Analyst
le 3 Juil 2021
What does this thing represent? What is the real world object you images to get this? Is the real object known to be a rectangle or cylinder, with straight sides, or does it have ragged sides?
Conor O'Keeffe
le 4 Juil 2021
Image Analyst
le 4 Juil 2021
@Conor O'Keeffe, after seeing your original gray scale image, I think Matt's solution is the one you should use and Accept.
DGM
le 4 Juil 2021
I think I'd agree with that.
Conor O'Keeffe
le 4 Juil 2021
Réponse acceptée
Plus de réponses (1)
I'll throw this out there. I'm assuming that the goal here is density-dependent (linear) mask constriction. On that assumption, I'm avoiding erosion and using an averaging filter and thresholding. It works, but it would likely require adjustment, considering I don't know what the particular limits are or what other images will look like.
% parameters
frad = 15;
masklevel = 0.1;
outlevel = 0.18;
% flattened, binarized image
inpict = rgb2gray(imread('capture.jpg'))>128;
% if you want to filter by local density, maybe use an avg filter
wpict = imfilter(double(inpict),fspecial('disk',frad));
% first pass to get rid of stray exterior points
mask = double(bwareafilt(wpict>masklevel,1));
wpict = wpict.*mask;
% second pass to tighten group following density
wpict = wpict>outlevel;
% as opposed to erosion which follows envelope
%wpict = imerode(wpict,strel('disk',10));
% for viewing, i'm just going to slap together a weighted mean
% you can use whatever you want. wpict is just a binary mask like any other.
k = 0.3;
comp = inpict*k + wpict*(1-k);
imshow(comp)

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