ECE4076 Computer Vision Lab 1



ECE4076 Computer Vision
Lab 1 (Weeks 3,4): Canny Edge Detection
Each task in the lab exercise is worth 2% of your final unit grade (total 10%). A task is only
considered complete if you can demonstrate a working program and show understanding of the
underlying concepts. Note that later tasks should reuse code from earlier tasks.
In this laboratory exercise, you will implement the Canny Edge detector (without hysteresis thresholding)
using matlab. Make sure you run your code on the test images provided to see if the results are as
expected before attempting to get marked.
Canny edge detector overview:
Test images are located in the lab1 folder. These should be used to test your code.
Task 1: Implement Gaussian blur
Write a program that performs Gaussian blur on an input image using the following 5×5 kernel, where B
is the blurred version of input image A. You code should not use the conv function in matlab. Show your
result on screen.
Task 2: Calculate image gradients
Write a program that does the following in order. You should reuse your Task 1 code
1. Load an image from the hard drive as a grayscale (single channel) image.
2. Blur the image using a 5×5 Gaussian filter
3. Calculate the gradient of the blurred image in the x and y directions using a 3×3 Sobel filter
4. Check the x and y gradient values to make sure they are correct
Task 3: Calculate gradient magnitude
Extend your program to calculate the gradient magnitude. Gradient magnitude G is a function of the
gradients in the x and y directions (Gx, Gy)
Show the gradient magnitude on screen as a grayscale image
Task 4: Calculate gradient orientation
Extend your program to calculate the gradient orientation as follows
ECE4076 Computer Vision
Note that gradient orientations should be rounded to the nearest 45-degrees (Compass directions: North,
North-East, East etc). Rounding can be performed using a combination of integer arithmetic and the
floor() function. Display the gradient orientations on screen to check that they are correct. You may want
to use colours to illustrate the different orientations.
Task 5: Non-maxima Suppression and thresholding
Finally, extend your code to perform non-maxima suppression in order to “thin” the edges. A detailed
summary is provided here. The idea is to zero any pixel that is not greater in terms of gradient magnitude
than both pixels on either side of its gradient orientation.
Threshold the non-maxima suppression results. The final output should be a black and white (binary)
image showing the edge pixels. Test your code against the test images.


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