Problem 2:
Gradient edge detection.
In this problem you will explore a very simple edge detector.
a. Load Image A and smooth it with a Gaussian filter to eliminate noise.
b. Use the gradient function to compute the 1st derivatives
in the x and y direction:
[dy,dx]=gradient(A);
c. Look at the magnitude of the gradient image using imshow,
M=sqrt(dx^2+dy^2)
d. Now use the command quiver(dy,dx) to view the actual gradient
vectors; you may want to zoom in. (The image will be upside down,
don't worry about it). It is very important that you understand what
is going on here for this and the next lab.
e. Create a binary edge image by thresholding the gradient magnitude
image. Pick a value that gives you few gaps in the edges.
f. Finally thin the edges using the following process:
1. Visit every edge pixel in the binary image.
For each one, look at the gradient direction at that point (dy,dx) and
round it to the nearest of the 8 pixel directions.
2. Look at the gradient magnitude of the pixel in
that direction and also at the pixel in the oposite direction.
3. If the pixel in question has a gradient magnitude
less than either of those two neighbors, set its value in the binary image
to 0.
Submit the thinned and unthinned images. Also try it on image
B.
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