answer all questions ,,, Edge detection methods are often compared by their abil
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answer all questions ,,,
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. Add noise to the test Image and extract Its edges. I_noise = imnoise(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'); How did the Prewitt edge detector perform in the presence of noise (compared to no noise)? Did MATLAB use a different threshold value for the noisy image? 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
Prewitt Edge Detection
It is a discrete differentiation operator. It computes the approximation of gradient intensity function. The result of
Prewitt operator is either the corresponding gradient vector or normal of this vector. It is based on the convolving the image with small, separable and integer valued filter in horizontal(x)
and vertical(y) direction. It is computationally less expensive and faster method for edge detection. It is only appropriate for noiseless and well contrasted images
[6]. The difference between Prewitt and Sobel operator is the spectoral response. It is an appropriate way to estimate
the magnitude and orientation of an edge.
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