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- 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 isComputer 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 separatelyPlease write python codes to smooth an image by using median filter and Bilateral filter separately. You should give your codes, the input image and its outputs.
- Integral image is obtained by summing all the pixels before each pixel (Naively you can think of this as similar to the cumulative distribution function where a particular value is obtained by summing all the values before). Let's take an example to understand this. Suppose we have a 5x5 binary image as shown below. The integral image is shown on the right. 2, 1, 2, 2], 1, 1, 3, 3], 5, 5], 8], [1, 2, 2, 5, [2, 4, 4, 8, [ 3, 5, 5, 9, 10]]. Integral image All the pixels in the integral image are obtained by summing all the previous pixels. Previous here means all the pixels above and to the left of that pixel (inclusive of that pixel). For instance, the 3 (blue circle) is obtained by adding that pixel with the above and left pixels in the input image i.e. 1+0+0+1+0+0+0+1 = 3. [[1, 0, 0, 1, 0], [0, 0, 0, 1, 0], [0, 1, 0, 1, 0], [1, 1, 0, 1, 0], [1, 0, 0, 0, 1]] Original image [[1, 1, 1, [1, Write a function integral_image () that takes as parameter an image in the form of a nested list A…Using Python, implement Canny Edge Detection algorithm. Apply your program to detect edges in an image. Your program will take as an input a grayscale image, the sigma of the Gaussian filter and the two low and high thresholds. The output of your program is a binary image.While doing Histogram Stretching, we established that we go pixel by pixel through whole image and apply Histogram Stretching Formula for each pixel. So, for example in the following image, if there are 100 pixels, we would do Histogram Stretching Formula 100 times. Do you think we could do Histogram Stretching for whole image and calculate this formula fewer times ? If no why ? If yes, how ?
- An 8-bit image has a perfectly normalized histogram. Suppose that each pixel value is ANDed with 1000 0000 , and is set to white if the result of ANDing is non-zero, otherwise is set to black if the result is zero. What is the normalized number of black pixels in the output image? Write the number with 2 decimal points in the box below.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 14.5Connect a 5*7 dot-matrix display to the 8051-microcontroller (Draw the schematic on paper and upload it as a photo). Then, display the content of variable NUMBER on the display, digit by digit every one second. for example if NUMBER=249, then "2" will be displayed for one second, after that digit "4" and finally digit "9" will be displayed for a whole second. Assume that the necessary lookup table exists in Memory at address 0xF000
- Although the plot function is designed primarily for plotting standard xy graphs, it can be adapted for other kinds of plotting as well. b. Make a plot of the curve, which is defined parametrically by the equations x = 2cosθ + cos2θ, y = 2sinθ - sin2θ, where 0 < θ < 2π. Take a set of values of θ between zero and 2π and calculate x and y for each from the equations above, then plot y as a function of x. b. Taking this approach a step further, one can make a polar plot r = f(θ) for some function f by calculating r for a range of values of θ and then converting r and θ to Cartesian coordinates using the standard equations x = r cosθ, y = r sinθ. Use this method to make a plot of the function r = ecosθ – 2 cos(4θ) + sin5 (θ/12) in the range 0 <= θ <= 24π. use python code to answer the highlight one2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises…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.