Q1. Image Downsampling:
(a) Downsample the image barbara.tif by a factor of 2. Downsample the image by selecting every second pixel in both directions (do not use library functions to downsample). What artifacts do you notice?
(b) Now, first filter the image with a spatial domain Gaussian Low Pass Filter before downsamplingthe image. You can use a 5 × 5 window and σ = 1. Also experiment with different window sizes and σ values. Do you notice the mitigation of the artifacts? Compare your result with a library function.
(10+15=25M)
Q2. Edge Detection: For the grayscale images Checkerboard.png, NoisyCheckerboard.png, Coins. png and NoisyCoins.png,
(a) Smooth the input image using a spatial domain Gaussian filter. You can use a 5 × 5 window and σ = 5.
(b) Use the Sobel/Prewitt operator to compute the image gradients, and then compute thegradient magnitude. Use thresholding to get the edges.
(c) For the same set of images, use Laplacian operator to detect the edges. For the noisy images,threshold the filtered image before finding the zero crossings.
Analyse the difference between first-order and second-order gradient-based edge detectors for clean and noisy images. (10+10+15=35M)
Q3. Interest Point Detection: For the images Checkerboard.png and MainBuilding.png,
(a) Implement Harris corner detector. Experiment with different threshold values and report yourresults.
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(b) Modify the images by rotating, scaling and adding noise. Now detect corners in the modifiedimages. Analyse the difference in performance as compared to the original images.
(15+15=30M)
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