Question

I am trying to understand this code:

d=edge(d,'canny',.6);
figure,
imshow(d,[])

ds = bwareaopen(d,40);
figure,
imshow(ds,[])

iout = d1;
BW=ds;

iout(:,:,1) = iout;
iout(:,:,2) = iout(:,:,1);
iout(:,:,3) = iout(:,:,1);
iout(:,:,2) = min(iout(:,:,2) + BW, 1.0);
iout(:,:,3) = min(iout(:,:,3) + BW, 1.0);

I understand that d is the image and canny detector is applied and 40 pixels are neglected. The image is gray scale and contour is added to the image.

Can you please explain the next lines? What principle/algorithm is used here? I am having trouble especially with the contour detection portion of the code.

Answer 1

Assuming that the variable d1 stores what is likely a double precision representation (values between 0 and 1) of the original grayscale intensity image that is operated on, then the last 5 lines will turn that grayscale image into a 3-D RGB image iout that looks the same as the original grayscale image except that the contours will be overlaid on the image in cyan.

Here’s an example, using the image 'cameraman.tif' that is included with the MATLAB Image Processing Toolbox:

d1 = double(imread('cameraman.tif'))./255;  % Load the image, scale from 0 to 1
subplot(2, 2, 1); imshow(d1); title('d1');  % Plot the original image
d = edge(d1, 'canny', .6);                  % Perform Canny edge detection
subplot(2, 2, 2); imshow(d); title('d');    % Plot the edges
ds = bwareaopen(d, 40);                     % Remove small edge objects
subplot(2, 2, 3); imshow(ds); title('ds');  % Plot the remaining edges
iout = d1;
BW = ds;
iout(:, :, 1) = iout;                           % Initialize red color plane
iout(:, :, 2) = iout(:, :, 1);                  % Initialize green color plane
iout(:, :, 3) = iout(:, :, 1);                  % Initialize blue color plane
iout(:, :, 2) = min(iout(:, :, 2) + BW, 1.0);   % Add edges to green color plane
iout(:, :, 3) = min(iout(:, :, 3) + BW, 1.0);   % Add edges to blue color plane
subplot(2, 2, 4); imshow(iout); title('iout');  % Plot the resulting image

And here is the figure the above code creates:

How it works…

The creation of the image iout has nothing to do with the edge detection algorithm. It’s simply an easy way to display the edges found in the previous steps. A 2-D grayscale intensity image can’t display color, so if you want to add colored contour lines to the image you have to first convert it to a format that will let you show color: either an indexed image (which is a little harder to deal with, in my experience) or a 3-D RGB image (the third dimension represents the red, green, and blue color components of each pixel).

Replicating the grayscale image 3 times in the third dimension gives us a 3-D RGB image that initially still contains gray colors (equal amounts of red, green, and blue per pixel). However, by modifying certain pixels of each color plane we can add color to the image. By adding the logical edge mask BW (ones where edges are and zeroes elsewhere) to the green and blue color planes, those pixels where the contours were found will appear cyan. The call to the function min ensures that the result of adding the images never causes a pixel color value to exceed the value 1.0, which is the maximum value an element should have for a double-precision 3-D RGB image.

It should also be noted that the code for creating the 3-D RGB image can be simplified to the following:

iout = d1;
iout(:, :, 2) = min(d1+ds, 1.0);
iout(:, :, 3) = min(d1+ds, 1.0);