In the attached image there are periodic vertical streaks which are random along the x axis and vary along the y axis in their intensity.
Any suggestions on how to detect these?
Ideally I would like to detect these with an output of a binary image showing the streaks (I imagine it will end up looking like a bar code).
Thanks!
You can use a simple horizontal filter to collect information about these lines:
I=imread('lines.jpg');
I=double(I)*(1.0/255.0);
J=filter2([-1 0 1;-1 0 1;-1 0 1],I);
s=sum(J);
Which results in the following 1D signal for the image.
You can then find the right threshold for you on this signal, and use the index as the line position.
Photon's answer is very good: he suggested alongated vertical edge filters to capture the vertical edges of the streaks.
However, if you are interested in locating the streaks themselves and not their edges, you might consider a slightly different approach:
I propose to, first, eliminate the "DC" component, so that the streaks will "pop out" of a roughly constant background, then use a vertical sum to locate them and produce a mask.
Here's a sketch of the code:
img = im2double( imread('https://i.stack.imgur.com/SqZrf.jpg') ); %// read the image
Use a horizontal filter to get an estimate of the local "DC", that is, an image where the streaks are gone:
dc_est = imfilter(img, ones(1,31)/31, 'symmetric' );
Looking at the difference between the image and the estimated "DC" should make the streaks pop out and easy to threshold using a global threshold:
global_thr = 0.025;
mask = ones(size(img,1),1)*(mean(img-dc_est,1)>global_thr);
figure; imshow(mask);
And heres' the result (you might want to change the threshold value and see how it affects the result):
The estimated "DC", dc_est looks like:
If you are after a more elaborate adventure, I recommend that you explore this work: I. Horev, B. Nadler, E. Arias-Castro, M. Galun, R.Basri Detection of long edges on a computational budget: a sub-linear approach (SIAM 2015). This method is aimed at finding these elusive edges and ridges in noisy intensity images.
