# s1 - #5 ECE 253 Digital Image Processing Pamela Cosman...

This preview shows pages 1–3. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: #5 ECE 253 Digital Image Processing Pamela Cosman 10/12/10 Solutions to Homework 1 1. Boundary extraction: The bwperim command will work: bw2 = bwmorph(bw,4); The remove command which is part of bwmorph produces the same result: bw3 = bwmorph(bw,’remove’); The approach given in class is to do an erosion, and then subtract the eroded version from the original image: bw6 = bwmorph(bw,’erode’,1); bw7 = bw - bw6; This gives us an inner boundary, that consists of pixels which are part of the original fore- ground connected components. You could get another version of a boundary by using the same approach with a different structuring element, for example a 5x5 structuring element which would give a thicker boundary, or a cross-shaped structuring elements which would give a thin 8-connected boundary. We can get an “outer boundary”, that consists of pixels which are not actually part of the original foreground connected components, by doing a dilation, and then subtracting the original image from the dilated version of it: bw4 = bwmorph(bw,’dilate’,1); bw5 = bw4 - bw; There are various other random things one could call a “boundary” for example do one round of thinning, and then subtract the thinned version from the original image: bw8 = bwmorph(bw,’thin’,1); bw9 = bw - bw8; But this one looks a little weird, which makes sense if you know what the thinning is doing. 2. Adding salt-and-pepper noise: For producing noise by random pixel flipping, the exclusive OR function allows a compact function: 1 function out = ipflip( in, p ) out = xor( in, rand( size(in) ) < (p/100) ); 3. Isolated pixel cleaning: Isolated pixel removal can be achieved several different ways. Here’s a method based on pixel stacking: function out = clean2( noisy ) mask = [ 1 8 64; 2 16 128; 4 32 256]; im2 = filter2(mask,noisy); test = (im2 ~= 16); out = noisy .* test; test = (im2 == 495); out = out + test; Visually, it looks like the low-noise case gets very well cleaned up, but the high-noise case doesn’t get cleaned much at all. This is because in the high-noise case, many of the noise pixels are not isolated.pixels are not isolated....
View Full Document

## This note was uploaded on 01/15/2012 for the course ECE ece253A taught by Professor Cosman during the Fall '11 term at UCSD.

### Page1 / 6

s1 - #5 ECE 253 Digital Image Processing Pamela Cosman...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online