Question

I am new to Matlab and to Image Processing as well. I am working on separating background and foreground in images like this

I have hundreds of images like this, found here. By trial and error I found out a threshold (in RGB space): the red layer is always less than 150 and the green and blue layers are greater than 150 where the background is.

so if my RGB image is I and my r,g and b layers are

redMatrix = I(:,:,1);
greenMatrix = I(:,:,2);
blueMatrix = I(:,:,3);

by finding coordinates where in red, green and blue the values are greater or less than 150 I can get the coordinates of the background like

[r1 c1] = find(redMatrix < 150);
[r2 c2] = find(greenMatrix > 150);
[r3 c3] = find(blueMatrix > 150);

now I get coordinates of thousands of pixels in r1,c1,r2,c2,r3 and c3.

My questions:

1. How to find common values, like the coordinates of the pixels where red is less than 150 and green and blue are greater than 150?
   I have to iterate every coordinate of r1 and c1 and check if they occur in r2 c2 and r3 c3 to check it is a common point. but that would be very expensive.
   Can this be achieved without a loop ?

2. If somehow I came up with common points like [commonR commonC] and commonR and commonC are both of order 5000 X 1, so to access this background pixel of Image I, I have to access first commonR then commonC and then access image I like

   I(commonR(i,1),commonC(i,1))

that is expensive too. So again my question is can this be done without loop.

Any help would be appreciated.

I got solution with @Science_Fiction answer’s

Just elaborating his/her answer

I used

mask = I(:,:,1) < 150 & I(:,:,2) > 150 & I(:,:,3) > 150;

Answer 1

Your approach seems basic but decent. Since for this particular image the background is composed of mainly blue so you be crude and do:

mask = img(:,:,3) > 150;

This will set those pixels which evaluate to true for > 150 to 0 and false to 1. You will have a black and white image though.

imshow(mask);

To add colour back

mask3d(:,:,1) = mask; 
mask3d(:,:,2) = mask; 
mask3d(:,:,3) = mask;

img(mask3d) = 255;
imshow(img);

Should give you the colour image of face hopefully, with a pure white background. All this requires some trial and error.