1.2 Consider the following CNN. An 8 x 8 x 3 image input, followed by a convolution layer with 2 filters ofsize 2 x 2 (stride 1, no zero padding), then another convolution layer with 4 filters of size 3 x 3 (stride 2, nozero padding), and finally a max pooling layer with a 2 x 2 filter (stride 1, no zero padding). i. How manyparameters do we need to learn for this network? Give two answer, with a bias neuron and without ii.What is the picture final dimension?

1.2 Consider the following CNN. An 8 x 8 x 3 image input, followed by a convolution layer with 2 filters of size 2 x 2 (stride 1, no zero padding), then another convolution layer with 4 filters of size 3 x 3 (stride 2, no

1.2 Consider the following CNN. An 8 x 8 x 3 image input, followed by a convolution layer with 2 filters of size 2 x 2 (stride 1, no zero padding), then another convolution layer with 4 filters of size 3 x 3 (stride 2, no

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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Consider the given image. Apply a non-linear filter that can effectively remove the salt and pepper noise and provide the filtered image. Assume border pixels are replicated for filter evaluation at the edges and kernel size is 3 x 3.
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Question 8. What are the resulting size number of activation maps (wrongly I called "layer"), when we have a starting image of 100x100 pixels, with 30 filters/kernel of size 20x20, stride 2 and padding 0 ?
A 3x3 kernel is sliding across an image (single channel image). At the current position the kernel stopped at a patch of pixels with the following values: 2 1 3 1 1 0 0 4 1 The kernel is defined as follows: 1 -1 1 0 2 0 1 -1 1 Compute the value that will go into the feature map for this convolution operation.
Question 3 Use a 3 x 3 mid-point filter to restore the noisy image matrix, g(x,y)?
posting multiple times dont answer if u dont know
dont pot existing or copied one sure dislike skip if dont know answer only 100% sure
Please explain in detail Given the calculated filter coefficients h (0) = 0.25, h (- 1) = h(1) = 0.22508, h(- 2) = h(2)= 0.15915, h(- 3) = h(3) = 0.07503, Apply the Hamming window function to obtain windowed coefficients hw(n).
Consider a 2d Convolutional Layer defined below. Given a 20x20 sized input image, this outputs 5 images of size x x x. nn. Conv2d (1, 5, 3) What's x?
we have a camera with a viewing plane located at (0,0). Viewing/Camera plane coordinates: u=(1,0), ve(0,1) I we rotate the camera plan by 90 degrees counter-clockwise, the point (2,3) in the imaging plane will be seen at which location with respect to transformed camera coordinates? ve(0.1) (2.3) rotate u(1,0) O 3.2) O (2.3) O 2-3) O 13.2)
Consider a convolution layer that processes an input image of size 256 × 256 x 3 and where the output has one channel. If the convolution layer contains a single 1 x 1 convolution filter, how many parameters are there including bias?
Problem 1. Download the file Square.tif and answer the following questions: i. Smooth the image with a 3x3 spatial filter. ii. Smooth the image with an implementation of Gaussian filter in frequency domain. Change the bandwidth of the filter and show how these changes affect the result. iii. Visualize that filtering in frequency domain can cause wraparound error in spatial domain and explain how can this interference be avoided. Apply your idea as a preprocessing step in part (ii) and display your results. iv. Sum up your conclusions in part (ii) and (iii), and List the Basic Steps required in DFT filtering. Implement all these steps in an M-file, named YOUfft2 (replace YOU with your name). This file must get an arbitrary image as input, smooth the image with a Gaussian filter, and give the filtered image in output. The process of filtering should be done in frequency domain. Problem 2. Download the file CA03.jpg. i.Add Gaussian noise N( z =0, o =10/256) to the image and then apply…