V1 According to the signature information in the tablebelow, find the class label for a pixel with DNvalues of 80 at band 1 and 85 at band 2, using twoclassification algorithms:1) Parallelepiped classification, and2) Minimum distance to means classification.BandBand 21MeanStandardМeanStandarcDeviationDeviationClass60100101Class70902Your answerTyped answers are easier for students to read thanhandwritten notes

Answered: Band Band 2 1 Mean Standard Мean…

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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Create client programmes that utilise your edge-weighted digraph to generate edge-weighted digraphs for a wide range of V and E values that have a significant portion of negative weights but only a few negative cycles.
"""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
Create client programmes that utilise your edge-weighted digraph to generate edge-weighted digraphs with a high percentage of negative weights but just a few negative cycles, for as wide a range of V and E values as feasible.
How about the bottom image answer?
Create client programmes that use your edge-weighted digraph to generate edge-weighted digraphs with a high percentage of negative weights but only a few negative cycles, for as wide a variety of V and E values as feasible.
For an object detection problem, assume you are designing a YOLO like model to do the job. Your input image size is 127x127 (RGB). We are looking for a 3x3 output grid size. The number of classes is 20 and for each cell in the grid, we are considering 2 anchors. Design the CNN network and as a designer feel free to set your network's hyper-parameters as you wish. The output of your network needs to show the final height, width, and the channel. You do not need to include post-processing (nan-max suppression) step for YOLO. In Python-
It will simulate the diffusion of heatthrough a square metal plate. The plate starts off at 0 deg., but the 4 ×4 pixel square in the centeris kept at 100 deg.Now a cell in the interior has 4 neighbors: the upper, lower, left, and right neighbor. The updateformula for an interior point (other than the center square) is therefore given by:u′i,j = ui,j + k((ui,j+1 −ui,j ) + (ui,j−1 −ui,j ) + (ui−1,j −ui,j ) + (ui+1,j −ui,j ))= ui,j + k(ui,j+1 + ui,j−1 + ui−1,j + ui+1,j −4ui,j )The boundaries have to be updated separately using 8 different equations: 4 for each of the edges,and 4 for each corner. Here is the update formula for a cell on the top edge that is not on a corner:u′i,n−1 = ui,n−1 + k((ui,n−2 −ui,n−1) + (ui−1,n−1 −ui,n−1) + (ui+1,n−1 −ui,n−1))= ui,n−1 + k(ui,n−2 + ui−1,n−1 + ui+1,n−1 −3ui,n−1)Here is the update formula for a cell in the top left corner:u′0,n−1 = u0,n−1 + k((u0,n−2 −u0,n−1) + (u1,n−1 −u0,n−1))= u0,n−1 + k(u0,n−2 + u1,n−1 −2u0,n−1) Program:
Suppose we have an input image f(x,y) shown below. 0 1 2 3 4 2 2 2 2 2 2 4 5 6 7 0 1 2 3 4 0 0 1 5 7 Plese compute the gradient at point (2,3) (Write ansewer in the first blank) (2,4) (Write answer in the second blank) (3,3) (Write ansewer in the third blank) (3,4) (Write answer in the fourth blank) using Sobel Operator. gradient (x,y) =|Gx|+|Gyl.
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?
Modify the Koch Snowflake project from Mindtap to draw a 2-Dimensional Sierpinski Carpet.
What would be the result of adding a positive constant (scalar) to a monochrome image? What would be the result of multiplying a monochrome image by a positive constant less than 1.0?

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Band Band 2 1 Mean Standard Мean Standard Deviation Deviation Class 60 100 10 1 Class 70 90 Your answer Typed answers are easier for students to read than handwritten notes LO 2.