What is the primary goal of computer vision?
a) To create 3D images
b) To enable computers to understand and interpret visual information
c) To enhance the quality of images
d) To make images more colorful
Which of the following techniques is commonly used in image recognition?
a) Linear regression
b) Convolutional neural networks (CNNs)
c) K-means clustering
d) Decision trees
What does a convolutional layer do in a convolutional neural network (CNN)?
a) Reduces the size of the image
b) Detects patterns and features in the image
c) Increases the image resolution
d) Classifies the image into categories
What is the purpose of image segmentation in computer vision?
a) To classify images
b) To divide an image into multiple segments for analysis
c) To reduce the resolution of an image
d) To remove noise from images
Which of the following is a challenge in image recognition?
a) The amount of available data
b) The computational power required
c) Variability in lighting and object orientation
d) Both b and c
What is object detection in computer vision?
a) Finding specific pixels in an image
b) Identifying and locating objects in an image or video
c) Enhancing image resolution
d) Converting images to grayscale
Which algorithm is often used for facial recognition?
a) K-means clustering
b) Support vector machine (SVM)
c) Haar cascades
d) Decision trees
What does an image’s pixel value represent in computer vision?
a) The color and intensity of a specific point in an image
b) The dimensions of the image
c) The image’s depth
d) The type of object in the image
What is feature extraction in computer vision?
a) The process of detecting objects in an image
b) The process of identifying relevant information (features) from an image for further processing
c) The process of reducing the size of an image
d) The process of enhancing the quality of images
What is the role of the pooling layer in CNNs?
a) To classify the image
b) To reduce the spatial dimensions of the image
c) To apply convolution operations
d) To add more complexity to the network
Which type of neural network is commonly used for image recognition tasks?
a) Recurrent neural networks (RNNs)
b) Convolutional neural networks (CNNs)
c) Generative adversarial networks (GANs)
d) Long short-term memory (LSTM) networks
What is a common application of image recognition in healthcare?
a) Predicting stock market trends
b) Detecting diseases and abnormalities in medical images
c) Enhancing the quality of audio signals
d) Recognizing speech patterns
What is the purpose of data augmentation in computer vision?
a) To make the images more complex
b) To artificially increase the size of the dataset by applying transformations like rotation and flipping
c) To reduce the computational cost of training
d) To create new features from the existing data
Which of the following is a method for handling the problem of overfitting in image recognition models?
a) Using a smaller dataset
b) Data augmentation
c) Reducing the complexity of the model
d) Both b and c
What is the significance of “transfer learning” in computer vision?
a) Using pre-trained models on new tasks with minimal data
b) Training models from scratch every time
c) Using models for image classification only
d) Applying convolutional layers only
What is the output of an image classification model?
a) A bounding box around the object
b) A list of all objects in the image
c) A predicted label for the image
d) A transformed version of the input image
What is the role of the activation function in CNNs?
a) To add noise to the images
b) To introduce non-linearity and help the model learn complex patterns
c) To reduce the size of the image
d) To convert grayscale images into color
Which of the following is true about image classification?
a) It is the task of recognizing the content of an image and labeling it accordingly
b) It is the task of enhancing the resolution of images
c) It is the task of detecting edges in an image
d) It is the task of transforming 3D images into 2D images
Which technique is often used in computer vision for detecting faces in images?
a) K-means clustering
b) Haar feature-based cascade classifiers
c) Support vector machines (SVMs)
d) Reinforcement learning
What is semantic segmentation in computer vision?
a) Assigning a label to every pixel in an image to classify the objects they belong to
b) Finding the boundaries of objects in an image
c) Identifying only the most important objects in an image
d) Reducing the resolution of an image
What is the main advantage of using CNNs for image recognition?
a) They require less computational power
b) They can automatically learn spatial hierarchies in images
c) They work better on sequential data
d) They eliminate the need for labeled data
Which technique in computer vision helps to recognize text in images?
a) Optical Character Recognition (OCR)
b) Image segmentation
c) Histogram equalization
d) Edge detection
What is the primary challenge of object tracking in video sequences?
a) Identifying the color of objects
b) Maintaining object identity across frames despite changes in appearance
c) Extracting features from each frame
d) Identifying the type of object in each frame
What is “edge detection” in image processing?
a) The process of enhancing the contrast in an image
b) The process of detecting sharp discontinuities in the image’s intensity
c) The process of converting an image to grayscale
d) The process of identifying objects in the image
What does “deep learning” in computer vision primarily refer to?
a) Using simple models for image recognition
b) Using shallow neural networks for image classification
c) Using deep neural networks with many layers to automatically learn from large amounts of image data
d) Using only handcrafted features for image classification
What is a potential application of image recognition in autonomous vehicles?
a) Detecting and recognizing road signs
b) Enhancing the resolution of the vehicle’s cameras
c) Reducing the speed of the vehicle
d) Ignoring obstacles in the environment
Which algorithm is typically used for image clustering?
a) K-means clustering
b) Linear regression
c) Logistic regression
d) Naive Bayes
What is the main advantage of using transfer learning in image recognition?
a) It requires no labeled data
b) It uses pre-trained models to solve new tasks with less data and training time
c) It only works for image classification
d) It makes models more complex
Which of the following is an example of a computer vision task?
a) Predicting stock prices
b) Translating text from one language to another
c) Identifying objects and their locations in an image
d) Understanding spoken language
