How to detect edges in a image, and create a mask (matlab)

Question

I am trying to do a task, but there are 2 problems. The first one, I must take an image, and then detect the edges on the surface.

This is the original image:

and this would be the result:

However, I got this:

I am using a very simple code:

filter=[1 2 1;0 0 0;-1 -2 -1];
image=imread('boat.jpg');
image_edge=filter2(filter,image);
imshow(image_edge);

As you can see, it is very easy but I don't have the same image. Is my filter wrong?

The second problem is the next: I don't know how to create a matrix of miximg coefficients, mask(x,y). This matrix has to be created based on the edges (value of 1 in flat areas and gradually decrease to 0 to edges). What command should I use?

Answer 1

NKN definitely got the point, the only thing I want to point out is that you're using the sobel kernel for the y direction, if you want to effectively perform the edge detection on the x axis the kernel is

Sobel_x = [1 0 -1; 2 0 -2; 1 0 -1];

Also note that there are many other better detector, such as the Canny one. I strongly suggest you to have a look at it.

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Regarding your second question, I'm not sure what you're asking, but it looks like you want a normalized gradient magnitude matrix. To get it you must first (surprise!) calculate the gradient matrix:

G=sqrt(G_x.^2 + G_y.^2);

Where G_x and G_y has been obtained convolving the image with Sobel_x and Sobel_y in your case. This matrix will contain the gradient magnitude for each pixel of the image on which you're detecting edges.

Then to get things in the interval [0,1] you just normalise the matrix:

G = G/max(G(:));

In this case you'll get the opposite of what you want (closer to 1 mean that the pixel is likely to be part of an edge), so you may just simply do G = 1-G.

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EDIT:

Also note that to get a decent looking binary result you want to threshold the gradient magnitude matrix.

For example I tried a threshold of 0.15 on the G before doing the 1-G.

G = G>0.15;
G = 1-G
imshow(G)

and the result with sobel is:

I'm sure you can do better, this is just an almost random threshold value to show you the result.

Higher the threshold, finer the features you're classifying as edges.

Answer 2

is your filter extracting both vertical and horizontal edges? Because the filter2 uses the cov2 function and to extract both vertical and horizontal edges you need to use cov2 twice in your code. check this page for conv2 example on vertical and horizontal edges.

You can also try edge command in MATLAB though it works over intensity images:

I = imread('boat.jpg');
BW1 = edge(I,'prewitt');  % or any other filters supported
imshow(BW1);

check this page for different kinds of edge filters.