Home Certification Computer Vision Basics Coursera Answers – Free Certificate

Computer Vision Basics Coursera Answers – Free Certificate

Computer Vision Basics Course Answers - Certificate Coursera

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Week 1: Computer Vision Basic Course Certification Answers : Coursera

Question 1: Computer vision includes which of the following?

  • Automatic extraction of features from images
  • All are correct
  • None are correct
  • Understanding useful information
  • Analysis of images

Question 2: The image acquisition devices of computer vision systems capture visual information as digital signals?

  • True
  • False

Question3: Correct Syntax to read image in MATLAB in current folder

  • var_image = imread(‘my_image.jpg’)
  • var_image = imread(‘my_image’)

Question4: Select the correct option to crop top-left 50×50 section from the image read below.

  • var_image = imread(‘my_image.jpg’)
  • cropped_section = var_image(0:50,0:50)
  • cropped_section = var_image(1:50,1:50)
  • cropped_section = var_image[0:50,0:50]
  • cropped_section = var_image[1:50,1:50]

Question5: What is initial data type of the image you read through imread function of MATLAB?

  • int8
  • double
  • uint8

Question6: I1 = imread(‘my_image.jpg’)

I2 = im2double(I1)

  • Scales the image intensity values from 0 to 1
  • Converts the image from uint8 to double format
  • The array dimensions remain same

Question7: Select the options which assigns height and width of an image correctly in MATLAB.

var_image = imread(‘my_image.jpg’)

  • [height,width] = size(var_image );
  • image_dimension = size(var_image );
    • height = image_dimension(1)
    • width = image_dimension(2)
  • [width,height] = size(var_image );
  • image_dimension = size(var_image );
    • width = image_dimension(1)
    • height = image_dimension(2)

Question8: Accessing Image Sub-Regions

img = imread(‘cameraman.tif’);

subimg1 = img(1:50,1:50);

subimg2 = img(  end -49 :end,  end -49 :end);

SSD = sum(sum((double(subimg1) – double(subimg2)).^2));

SSD = immse(subimg1, subimg2) * numel(subimg1);


Week 2: Computer Vision Basic Course Certification Answers : Coursera

Question9: Which of the following are area sources?

  • Bulb
  • All of these
  • Sun at infinity
  • Diffuser boxes
  • White walls

Question10: Does distance of the light source affect the color of a pixel?

  • No
  • Yes

Question10: We lose depth information in perspective projection.

  • True
  • False

Question 11: Match column A with correct options in column in B

Column AColumn B
1) Shutter speeda) Amount of light per unit area reaching image sensor
2) Exposureb) an effect that causes different signals to become indistinguishable when sampled
3) Aperturec) The length of time when sensor is exposed to light when taking a photograph
4) Aliasingd) Hole or an opening through which light travels

Answer: 1-c, 2-a, 3-d, 4-b

Question 12: Color Imaging – RGB Channels

%Read the image

img = imread(‘image.jpg’);

%Get the size (rows and columns) of the image

[r,c] = size(img);


%Wrire code to split the image into three equal parts and store them in B, G, R channels




%concatenate R,G,B channels and assign the RGB image to ColorImg variable

ColorImg(:,:,1) = R;

ColorImg(:,:,2) = G;

ColorImg(:,:,3) = B;


Week 3: Computer Vision Basic Course Certification Answers : Coursera

Three-Level Paradigm

Question 13

Column AColumn B
1) Computational Theorya) Steps for Computation
2) Representation and algorithmb) Physical realization of algorithms, programs and hardware
3) Implementationc) What the device is supposed to do

Answer: 1-c, 2-a, 3-b

Question 14: Low-level vision consists of:

1) feature detection and matching

2) early segmentation

  • 1
  • 1 and 2
  • 2
  • None

Question 15 : Image Gradient Magnitude

img = imread(‘cameraman.tif’);

[Gx, Gy] = imgradientxy(img);

[Gmag, Gdir] = imgradient(Gx, Gy);

%Uncomment the code below to visualize Gx and Gy


%Uncomment the code below to visualize Gmag and Gdir


Week 4: Computer Vision Basic Course Certification Answers : Coursera

Question 16: Match the Algorithms in column A with correct techniques in column B

Column AColumn B
1) Dynamic Programminga) Binary Image Restoration
2) Graph algorithmsb) Stereo matching
3) Dynamic Programmingc) Seam Carving
4) Graph algorithmsd) Image segmentation

Answers: 1-b, 2-d, 3-c, 4-a

Question 17: Aligning RGB Channels (using SSD)

img = imread(‘course1image.jpg’);

[height, width] = size(img);

oneThird = floor(height/3);

B = img(1:(oneThird), :);

G = img((oneThird+1):(2*oneThird), :);

R = img((2*oneThird+1):(3*oneThird), :);

c_x = (341/2-25);

c_y = (400/2-25);

ref_img_region = double(G(c_x:c_x + 50, c_y:c_y + 50));

red_offset = offset(double(R(c_x:c_x + 50, c_y:c_y + 50)), ref_img_region);

shifted_red = circshift(R, red_offset);

blue_offset = offset(double(B(c_x:c_x + 50, c_y:c_y + 50)), ref_img_region);

shifted_blue = circshift(B, blue_offset);

ColorImg_aligned = cat(3, shifted_red, G, shifted_blue);

%ColorImg_aligned = cat(3, G, shifted_red, shifted_blue);

%ColorImg_aligned = cat(3, G, shifted_blue, shifted_red);

%ColorImg_aligned = cat(3, G, shifted_blue, shifted_red);


% Find the minimun offset by ssd

function [output] = offset(img1, img2)

    MIN = inf;

    for x = -10:10

        for y = -10:10

            temp = circshift(img1, [x, y]);

            ssd = sum((img2 – temp).^2, ‘all’);

            if ssd < MIN

                MIN = ssd;

                output = [x, y];






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