Enclosing Boundary - for blobs
Show older comments
Hi all
Is it possible to get the boundary central more dense region - ignoring the blobs on the side
6 Comments
DGM
on 3 Jul 2021
You might want to describe the method you're currently using.
Image Analyst
on 3 Jul 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
on 4 Jul 2021
Image Analyst
on 4 Jul 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
on 4 Jul 2021
I think I'd agree with that.
Conor O'Keeffe
on 4 Jul 2021
Accepted Answer
More Answers (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)

Categories
Find more on Convert Image Type in Help Center and File Exchange
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!