The input image has been converted into a matrix of size 28 X 28 and a kernel/filter of size 3 X 3 with a stride of 2 and padding of 1. What will be the size of the convoluted matrix? A. 15 x 15 B. 14 x 14 C. 14 x 15 D. 14.5 x 14.5
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The input image has been converted into a matrix of size 28 X 28 and a kernel/filter of size 3 X 3 with a stride of 2 and padding of 1. What will be the size of the convoluted matrix?
| A. |
15 x 15 |
|
| B. |
14 x 14 |
|
| C. |
14 x 15 |
|
| D. |
14.5 x 14.5 |
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- Assume that I is a grayscale image, I1 is obtained by applying histogram equalization on I, and I2 is obtained by applying histogram equalization on I1. Which of the following statements is DEFINITELY TRUE?"""You are given an n x n 2D mat representing an image. Rotate the image by 90 degrees (clockwise). Follow up:Could you do this in-place?""" # clockwise rotate# first reverse up to down, then swap the symmetry# 1 2 3 7 8 9 7 4 1# 4 5 6 => 4 5 6 => 8 5 2# 7 8 9 1 2 3 9 6 3 def rotate(mat): if not mat: return mat mat.reverse() for i in range(len(mat)): for j in range(i): mat[i][j], mat[j][i] = mat[j][i], mat[i][j] return mat if __name__ == "__main__": mat = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] print(mat) rotate(mat) print(mat).Computer Science For an image represented by a given large 3d numpy array, use numpy to get a neighborhood filter using the mean of the pixels. For example, look through each of the image’s 3×3 window of pixels and replace outlier pixels with the mean value of the others in the 3×3 window. Set the resulting pixel value to be the mean value of a pixel and its eight neighbors. For pixels on an edge, use the mean value of the pixel and its five neighbors. For pixels on corners, use the mean value of the pixel and its three neighbors. If you loop over every pixel then you must store your input and output arrays separately
