Introduction to Computer Vision and Image Processing Quiz Answers

All Weeks Introduction to Computer Vision and Image Processing Quiz Answers

Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries.

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Introduction to Computer Vision and Image Processing Week 01 Quiz Answers

Graded Quiz: Overview of Computer Vision and its Applications

Q1. Detecting dangerous items in an X-Ray is an application of computer vision

  • True
  • False

Q2. In the video lecture, what methodology is presented to detect rust on iron bridges?

  • A person on the ground takes multiple high-resolution images from multiple places. A computer vision expert splits these images into smaller groups. Each of the smaller images is passed to a custom classifier that can detect the presence of the metal structure versus other, non-metal structures. After this, the images are passed through another custom classifier that is trained to detect the presence of rust in images.
  • A person on the ground takes multiple high-resolution images from the same place. A computer vision expert splits these images into smaller groups. Each of the smaller images is passed to a custom classifier that can detect the presence of the metal structure versus other, non-metal structures. After this, the images are passed through another custom classifier that is trained to detect the presence of rust in images.
  • A person on the ground takes multiple high-resolution images from the same place. A computer vision expert splits these images into smaller groups. Each of the smaller images is passed to a custom classifier that can detect the presence of the metal structure.
  • A person on the ground takes multiple high-resolution images from multiple places. A computer vision expert splits these images into smaller groups. Each of the smaller images is passed to a custom classifier that can detect the presence of the metal structure.

Q3. The popularity of self-driving cars has been rising at an exponential rate over the past decade. Based upon what you have learned, which of the following computer vision technique(s) is useful for self-driving cars? Select all relevant answers

  • Object Detection
  • Motion Transfer
  • Image Classification
  • All of the above

Introduction to Computer Vision and Image Processing Week 02 Quiz Answers

Graded Quiz: Image Processing

Q1. What is linear filtering?

  • It is a standard way to filter text
  • It is a standard way to add text data
  • It is a standard way to filter Images using convolution
  • None of the above

Q2. The order of channels in OpenCV

  • GRB
  • BRG
  • BGR
  • R​GB

Q3. What type of image operation can ​convolution perform?

  • Edge detection
  • Sharpening
  • Blurring
  • All of the above

Q4. T​he height of the image is the:

  • N​umber of columns
  • N​umber of rows
  • N​umber of pixels
  • N​one of the above

Q5. Translation is

  • S​hrinking or expanding the image in a horizontal and/or vertical direction
  • S​hifting the image
  • R​otating the images

Introduction to Computer Vision and Image Processing Week 03 Quiz Answers

Graded Quiz: Image Classification

Q1. How many colour channels does a Grayscale image have ​?

  • 1
  • 2
  • 3​
  • 4

Q2. Which of the following generate Histograms for each region of an image separately using gradients and the orientation of pixel values?

  • Support Vector Machines
  • K-Nearest Neighbor
  • Histogram of Oriented Gradients
  • Softmax

Q3. Support Vector Machines for Image classification may use a kernels. There are different types of Kernels, which of the following is not a type of Kernel?

  • Linear
  • Polynomial
  • Radial Basis Function
  • Histogram of Oriented Gradients

Q4. In a sequence of array, what does the argmax function return?

  • The sequence of array in ascending order
  • The index corresponding to the maximum value
  • The index corresponding to the minimum value
  • It will return ​0

Q5. You train a Support Vector Machine and obtain an accuracy of 100% on the training data and 50% on the validation data. This is an example of:

  • Overfitting
  • Underfitting
  • A good model

Introduction to Computer Vision and Image Processing Week 04 Quiz Answers

Graded Quiz: Neural Networks

Q1. Which of the following are types of CNN architecture? Check all that apply:

  • RavNe
  • VGGNet
  • AlexNet
  • JoNet

Q2.

Look at the figure below, what kind of activation function is this:​

  • Sigmoid
  • ReLU
  • L​ogistic function

Q3. Which of the following is the size of the region in the input that produces a pixel value in the activation map:

  • Convolutional Neural Network
  • Activation function
  • ReLU
  • Receptive field

Q4. What makes a neural network a deep neural network?

  • Having one hidden layer
  • Having no hidden layer
  • An overfitting model
  • Having more than one hidden layer

Q5. A lot of times, we don’t have the time and resources to build our own model, so we use pre-trained CNN’s to speed up the process. This is best described as..

  • Transfer learning
  • Deep learning
  • SVM
  • AlexNet

Introduction to Computer Vision and Image Processing Week 05 Quiz Answers

Graded Quiz: Object Detection

Q1. Which of these are a problem of sliding windows? Select all that apply

  • Aspect Ratio
  • Object sizes
  • Overlapping objects
  • Grayscale image

Q2.

The following image with the bounding box is an example of

  • Object detection
  • Classification+Localization
  • Filtering
  • Classification

Q3. When we are dealing with object detection, there are many different classifiers that we can use. Which of the following classifiers is trained on a large number of images that include the object we are trying to detect as well as images that do not contain the object we are trying to detect?

  • Cascade Classifiers
  • Sliding window Classifiers
  • Viola Classifiers
  • Integral classifiers

Q4. Consider the actual bounding box in red and the predicted bounding box in blue. What loss would you use to determine the performance of your model’s output?

  • It saves it for a different algorithm
  • Classification loss
  • Cross-entropy loss
  • Squared loss

Q5. In object detection, the score:

  • gives you the probability of each class
  • is the confidence of the model prediction
  • is the bounding box region
  • saves it for a different algorithm
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