Introduction to Computer Vision and Image Processing Quiz Answers

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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