There are two parts in this homework, each part is divided into two parts, a and b:
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Part 1a: This part implements template matching using four different similarity measures (correlation, zero-mean correlation, sum of squared differences, and normalized cross-correlation). It takes an input image and a template image, calculates the similarity scores, and visualizes the results as heatmaps. It also identifies and marks the location with the highest similarity for each measure. -
Part 1b: The same functions as part 1a were used, only input2 was used as the input image. -
Part 2a: This part demonstrates the application of Butterworth low-pass filters for noise reduction. It displays both the original image and its Fourier Transform magnitude. It also shows the effects of Butterworth filters with different parameters (n and D0) on the image, displaying the filter's magnitude, the filtered result in the frequency domain, and the corresponding spatial domain output. -
Part 2b: This part demonstrates the application of Butterworth high-pass filters for edge detection. It displays both the original image and its Fourier Transform magnitude. It proceeds to apply Butterworth high-pass filters with varying parameters (n and D0) in the frequency domain, presenting the filter, magnitude spectrum, and the resulting filtered image for each parameter combination.