Description
1. You are required to implement the canny edge detection algorithm as described in class. Please
follow the steps mentioned below.
(a) Read the image ‘clown.jpeg’ and display it.
(b) Convert it into a grayscale image.
(c) To suppress the noise use a Gaussian kernel to smoothen it. Keep the kernel size as 5 × 5
and sigma = 1.5.
(d) Apply the standard Sobel operator Gx and Gy discussed in class. Display the filtered
outputs. Also show the gradient magnitude and angle images.
(e) Apply non-maximum suppression as discussed in class. The exact method requires computing the imaginary pixels shown in gray in the image below using interpolation methods
such as bilinear and bicubic interpolation. As a simplification just locate the image pixels
1
Figure 1: The exact method requires you to do interpolation. As an alternative adopt the simplified
approach described in Step (d).
which are spatially closest to the imaginary gray pixels and use those pixels as the neighboring pixels for non-maxima suppression. This way you can bypass the interpolation
step and have a computationally lighter method if you so desire. Show the Non-Maximum
Suppression output.
(f) Instead of using the Double thresholding Hysteresis used in the exact algorithm simply
do a single thresholding operation. Pixels with values less than the threshold should be
suppressed. Use the median value of the magnitude image computed in Step 4 as the
threshold value. Show the final output.
2. Repeat the above question but increase the sigma of the Gaussian kernel to 3. Choose your
filter size appropriately. Just show the final output for this question. How does the final output
differ from that of the previous question?
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