An input image has the size of 140x140 and we use a filter (kernel) with the size of 20x20. We then convolve the input image with the filter. a. If we do not use padding and stride is 1, the output size will be axa, where a is b. If we use padding of 5 with stride 1, the output size will be bxb, where b is c. If we use no padding and stride is 5, the output size will be cxc, where c is d. If we use padding of 5 with the stride 5, the output size will be dxd, where d is

An input image has the size of 140x140 and we use a filter (kernel) with the size of 20x20. We then convolve the input image with the filter. a. If we do not use padding and stride is 1, the output size will be axa, where a is b. If we use padding of 5 with stride 1, the output size will be bxb, where b is c. If we use no padding and stride is 5, the output size will be cxc, where c is d. If we use padding of 5 with the stride 5, the output size will be dxd, where d is

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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For an image of size 32x32 and a filter of size 5x5, if you want the convolution result to keep the original image size, what's the padding size in pixels? a) 5 b) 2 c)10 d) 3 explain.
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"""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).
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