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- Download a gray scale image from the internet. Load it in a matrix in your program. Reshape it to 50 x 50. Show the 50x 50 matrix. Can you see the values? (Using Numpy)For three objects, sorted from front to back with the colors C0 = 0.2, C1 = 0.8, C2 = 0.5 and the background color Cbg= 1(white). Compute the final pixel color by front to back and back to front compositing for the following alpha values α0=0.2, α1=0.4, α2=0.1. For the background, you can assume αbg = 1 (fully opaque). You can think of the background as a fourth layer.For the following 2 images, both have 8x8 size with black and white pixels. Although they look quite different, their histograms are identical. Suppose that both images are blurred with a 3x3 smoothing masks with zeros edge extension, would the new histograms still be the same? Explain your answer. 2.
- How about the bottom image answer?Given the following texture on the left side that mapped onto two tringles on the right as follows: For each of 4 rendered images given below. Give the texture coordinates associated with the vertices of each triangle. Hint: to write the texture coordinates associated with the vertix, you can write for example: V(0,0) with T(0,0), where V represents the vertix and T represents the texture coordinate. Your answer should be like the following example: a) V(0,0) with T(0,0), ..... , .... , ... etc. Follow the same format for b, c, and dThe attatched image is the 2D profile of an image, showing I(x,y). True or False: The spatial frequency in the x direction (kx) is π; the spatial frequency in the y direction (ky) is π; and the inital phase is π. To create this 2D intensity, we need to add together multiple 2D sine waves. The Fourier transformation of this image will contain a single peak at a single frequency (not counting peaks at zero and the overtones) The wave in the 2D profile is plotted for 3 wavelengths
- Perform histogramequalization and apply the method to get dark pixel domination in the following image tile. 15 15 15 15 7 3 11 10 3 15 15 10 3 15 10 9. 3 4 11 4 15 15 342. Binary image, X and structuring element, B, are given as follows: origin X B Compute the gradient image.For the following 2D image apply the 3x3 Maximum Filter with zero padding
- Perform region growing based segmentation using N4 neighborhood for threshold value 15 based on the specified starting points (seed point: dark pixel) for the image segment given below. Indicate segmented pixels by painting the tiles black. (Your quick response is important to me, thank you very much in advance.)Task 1: You will consider the effects of additive noise and the use of DFT to remove this kind of noise. The noisy image has been generated by adding noise in the form of a cosine function. Using frequency domain filtering, devise a procedure for removing the noise and show your results. For comparison purposes, remove the noise in the spatial domain by convolving the noisy image with Average filter (e.g., 7 x 7 and 15 x 15). Compare resulting images of both spatial and frequency domain filtering. Also, you can elaborate on differences in the jupyter notebook as a comment.Consider the two image subsets, S, and S, in the following figure. With reference to Section 2.5, and assuming that V = {1}, determine whether these two subsets are: (a)* 4-adjacent. (b) 8-adjacent. (c) m-adjacent. S, 0. 1 1: 0 1 0: 0 0. 1 1 1 0: 0 1 1 1 0 0. 0: 0 1. 1 1 0 0 1 1 1 Section 2.5 was covered in the "Pixel Relationships" lecture.