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A. Write a function that takes an image as input and runs the Sobel edge detector (described in the background document) and returns 3 images: the horizontal edges, the vertical edges and the combined result. B. Start a new cell and read in the image of a dragonfly provided, and convert it to grayscale, as follows: from skimage import color #Load the image and convert to grayscale image = color.rgb2gray(plt.imread('dragonfly.jpg')) Note that the variable ‘image' will have normalized 8-bit (0 - 1) pixel brightness values. Run the edge detector function on the dragonfly image and display the original with the 3 outputs in a 2 by 2 figure. C. Define a 10x10 kernel where all elements have value 0.01. This impulse response is giving an average over the 10x10 window, and it should have a smoothing effect on the image, similar to the moving window for the time signal. Convolve the image with this filter using ndimage.convolve(). Plot the original and the smoothed image side by side. You should notice that smoothing blurs the image a little. D. Run the edge detector function on the smoothed dragonfly image and plot the result side- by-side with the result from the edge detector on the original image. Report discussion: Describe the differences in the results using the edge detector on the original vs. smoothed image. Comment on how the results change if you use a larger size smoothing filter.

A. Write a function that takes an image as input and runs the Sobel edge detector (described in the background document) and returns 3 images: the horizontal edges, the vertical edges and the combined result. B. Start a new cell and read in the image of a dragonfly provided, and convert it to grayscale, as follows: from skimage import color #Load the image and convert to grayscale image = color.rgb2gray(plt.imread('dragonfly.jpg')) Note that the variable ‘image' will have normalized 8-bit (0 - 1) pixel brightness values. Run the edge detector function on the dragonfly image and display the original with the 3 outputs in a 2 by 2 figure. C. Define a 10x10 kernel where all elements have value 0.01. This impulse response is giving an average over the 10x10 window, and it should have a smoothing effect on the image, similar to the moving window for the time signal. Convolve the image with this filter using ndimage.convolve(). Plot the original and the smoothed image side by side. You should notice that smoothing blurs the image a little. D. Run the edge detector function on the smoothed dragonfly image and plot the result side- by-side with the result from the edge detector on the original image. Report discussion: Describe the differences in the results using the edge detector on the original vs. smoothed image. Comment on how the results change if you use a larger size smoothing filter.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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A. Write a function that takes an image as input and runs the Sobel edge detector (described in the background document) and returns 3 images: the horizontal edges, the vertical edges and the combined result. B. Start a new cell and read in the image of a dragonfly provided, and convert it to grayscale, as follows: from skimage import color #Load the image and convert to grayscale image = color.rgb2gray(plt.imread('dragonfly.jpg')) Note that the variable ‘image' will have normalized 8-bit (0 - 1) pixel brightness values. Run the edge detector function on the dragonfly image and display the original with the 3 outputs in a 2 by 2 figure. C. Define a 10x10 kernel where all elements have value 0.01. This impulse response is giving an average over the 10x10 window, and it should have a smoothing effect on the image, similar to the moving window for the time signal. Convolve the image with this filter using ndimage.convolve(). Plot the original and the smoothed image side by side. You should notice that smoothing blurs the image a little. D. Run the edge detector function on the smoothed dragonfly image and plot the result side- by-side with the result from the edge detector on the original image. Report discussion: Describe the differences in the results using the edge detector on the original vs. smoothed image. Comment on how the results change if you use a larger size smoothing filter.
Transcribed Image Text:A. Write a function that takes an image as input and runs the Sobel edge detector (described in the background document) and returns 3 images: the horizontal edges, the vertical edges and the combined result. B. Start a new cell and read in the image of a dragonfly provided, and convert it to grayscale, as follows: from skimage import color #Load the image and convert to grayscale image = color.rgb2gray(plt.imread('dragonfly.jpg')) Note that the variable ‘image' will have normalized 8-bit (0 - 1) pixel brightness values. Run the edge detector function on the dragonfly image and display the original with the 3 outputs in a 2 by 2 figure. C. Define a 10x10 kernel where all elements have value 0.01. This impulse response is giving an average over the 10x10 window, and it should have a smoothing effect on the image, similar to the moving window for the time signal. Convolve the image with this filter using ndimage.convolve(). Plot the original and the smoothed image side by side. You should notice that smoothing blurs the image a little. D. Run the edge detector function on the smoothed dragonfly image and plot the result side- by-side with the result from the edge detector on the original image. Report discussion: Describe the differences in the results using the edge detector on the original vs. smoothed image. Comment on how the results change if you use a larger size smoothing filter.
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