anwers all questions.... Practical Image and Video Processing Using MATLAB CH14
ID: 2081764 • Letter: A
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anwers all questions.... Practical Image and Video Processing Using MATLAB CH14
Edge detection methods are often compared by their ability to detect edges in noisy images. Let us perform the Prewitt operator on the Lenna image with additive Gaussian noise. 3. Add noise to the test image and extract its edges. I noise immoise (I, 'gaussian'); I prw2, t2] edge (I noise prewitt subplot (2,2,3) imshow (I noise) title Image w/ noise subplot (2,2, 4) imshow (I prw2), title Prewitt on noise Question 2 How did the Prewitt edge detector perform in the presence of noise (compared to no noise)? Question 3 Did MATLAB use a different threshold value for the noisy image? Question 4 Try using different threshold values. Do these different values affect the operator's response to noise? How does the threshold value affect the edges of the object?Explanation / Answer
10.) BW = edge(I) takes a grayscale or a binary image I as its input, and returns a binary image BW of the same size as I, with 1's where the function finds edges in I and 0's elsewhere.
By default, edge uses the Sobel method to detect edges but the following provides a complete list of all the edge-finding methods supported by this function:
The parameters you can supply differ depending on the method you specify. If you do not specify a method, edge uses the Sobel method.
To find edges, you can use the edge function. This function looks for places in the image where the intensity changes rapidly, using one of these two criteria:
Places where the first derivative of the intensity is larger in magnitude than some threshold
Places where the second derivative of the intensity has a zero crossing
edge provides several derivative estimators, each of which implements one of these definitions. For some of these estimators, you can specify whether the operation should be sensitive to horizontal edges, vertical edges, or both. edge returns a binary image containing 1's where edges are found and 0's elsewhere.
The most powerful edge-detection method that edge provides is the Canny method. The Canny method differs from the other edge-detection methods in that it uses two different thresholds (to detect strong and weak edges), and includes the weak edges in the output only if they are connected to strong edges. This method is therefore less likely than the others to be fooled by noise, and more likely to detect true weak edges.
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