Computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world, such as images and videos, by mimicking human visual processing. It involves extracting meaningful data from digital images to perform tasks like object detection, facial recognition, and scene understanding.
The origins of computer vision trace back to the 1960s, with early research focusing on basic image processing techniques. Over time, advancements in machine learning, particularly deep learning and neural networks, have revolutionized the field. For instance, convolutional neural networks (CNNs) have become essential for feature extraction and pattern recognition in complex visual data.
Key techniques in computer vision include image preprocessing (e.g., filtering and edge detection), feature extraction (e.g., using SIFT or HOG algorithms), and advanced models like generative adversarial networks (GANs) for image synthesis. These methods allow systems to classify objects, segment images, track motion, and even generate realistic visuals.
Applications span numerous industries: in healthcare, it’s used for medical imaging to detect diseases; in autonomous vehicles, for object recognition and navigation; in retail, for inventory management and customer analytics; and in security, for surveillance and biometric identification. Computer vision also powers augmented reality (AR) and virtual reality (VR) experiences.
Despite its progress, challenges remain, such as handling variations in lighting, occlusions, and real-time processing. The future of computer vision is promising, with ongoing developments in edge computing and ethical AI aiming to make systems more robust, efficient, and fair.
Table of contents
- Part 1: Create an amazing computer vision quiz using AI instantly in OnlineExamMaker
- Part 2: 20 computer vision quiz questions & answers
- Part 3: AI Question Generator – Automatically create questions for your next assessment
Part 1: Create an amazing computer vision quiz using AI instantly in OnlineExamMaker
Nowadays more and more people create computer vision quizzes using AI technologies, OnlineExamMaker a powerful AI-based quiz making tool that can save you time and efforts. The software makes it simple to design and launch interactive quizzes, assessments, and surveys. With the Question Editor, you can create multiple-choice, open-ended, matching, sequencing and many other types of questions for your tests, exams and inventories. You are allowed to enhance quizzes with multimedia elements like images, audio, and video to make them more interactive and visually appealing.
Recommended features for you:
● Prevent cheating by randomizing questions or changing the order of questions, so learners don’t get the same set of questions each time.
● Automatically generates detailed reports—individual scores, question report, and group performance.
● Simply copy a few lines of codes, and add them to a web page, you can present your online quiz in your website, blog, or landing page.
● Offers question analysis to evaluate question performance and reliability, helping instructors optimize their training plan.
Automatically generate questions using AI
Part 2: 20 computer vision quiz questions & answers
or
1. What is the primary purpose of edge detection in computer vision?
A. To enhance image colors
B. To identify object boundaries
C. To reduce image noise
D. To increase image resolution
Answer: B
Explanation: Edge detection algorithms like Canny or Sobel detect discontinuities in image intensity, which helps in identifying object boundaries for further analysis.
2. Which technique is commonly used for image segmentation?
A. K-means clustering
B. Linear regression
C. Decision trees
D. Support vector machines
Answer: A
Explanation: K-means clustering groups pixels based on similarity in features like color or intensity, making it effective for dividing an image into segments.
3. What does CNN stand for in the context of computer vision?
A. Conventional Neural Network
B. Convolutional Neural Network
C. Centralized Neural Network
D. Complex Neural Network
Answer: B
Explanation: Convolutional Neural Networks are designed to process grid-like data, such as images, by applying filters to detect features like edges and textures.
4. In computer vision, what is the role of optical flow?
A. To detect colors in images
B. To track motion between video frames
C. To compress video files
D. To enhance image sharpness
Answer: B
Explanation: Optical flow estimates the motion of objects between consecutive frames by analyzing pixel displacement, which is useful for video analysis and object tracking.
5. Which filter is used for blurring images to reduce noise?
A. Sobel filter
B. Gaussian filter
C. Laplacian filter
D. Prewitt filter
Answer: B
Explanation: The Gaussian filter applies a bell-shaped kernel to smooth images, effectively reducing high-frequency noise while preserving overall structure.
6. What is the main advantage of using Hough Transform?
A. Detecting straight lines in noisy images
B. Enhancing image colors
C. Compressing data
D. Classifying objects
Answer: A
Explanation: Hough Transform is robust for detecting shapes like lines and circles by transforming the image into a parameter space, even in the presence of noise.
7. Which metric is commonly used to evaluate object detection models?
A. Mean Absolute Error
B. Intersection over Union (IoU)
C. Root Mean Square Error
D. Accuracy score
Answer: B
Explanation: IoU measures the overlap between predicted and ground truth bounding boxes, providing a precise evaluation of localization accuracy in object detection.
8. What is the purpose of histogram equalization in image processing?
A. To change image colors
B. To improve contrast by spreading out intensity values
C. To detect edges
D. To resize images
Answer: B
Explanation: Histogram equalization adjusts the intensity distribution of an image to enhance contrast, making details more visible in both dark and bright areas.
9. In feature extraction, what does SIFT stand for?
A. Scale-Invariant Feature Transform
B. Simple Image Feature Technique
C. Scaled Image Filtering Tool
D. Standard Invariant Feature Tracker
Answer: A
Explanation: SIFT detects and describes local features in images that are invariant to scale, rotation, and affine transformations, aiding in object recognition.
10. Which algorithm is associated with face detection?
A. K-nearest neighbors
B. Viola-Jones algorithm
C. Random forest
D. Gradient descent
Answer: B
Explanation: The Viola-Jones algorithm uses Haar-like features and a cascade of classifiers to quickly detect faces in images with high accuracy.
11. What is the key benefit of transfer learning in computer vision?
A. It requires less data for training new models
B. It eliminates the need for GPUs
C. It only works on grayscale images
D. It increases computational speed without accuracy loss
Answer: A
Explanation: Transfer learning reuses pre-trained models on new tasks, allowing effective training with smaller datasets by leveraging learned features.
12. Which type of neural network layer is used for downsampling in CNNs?
A. Convolutional layer
B. Pooling layer
C. Fully connected layer
D. Activation layer
Answer: B
Explanation: Pooling layers, such as max pooling, reduce the spatial dimensions of feature maps, helping to make the model invariant to small translations.
13. What does OCR stand for in computer vision applications?
A. Optical Character Recognition
B. Object Classification Routine
C. Online Content Retrieval
D. Output Control Regulation
Answer: A
Explanation: OCR converts images of text into machine-encoded text, enabling applications like digitizing documents by recognizing characters.
14. In image processing, what is morphological erosion used for?
A. To add pixels to object boundaries
B. To remove pixels from object boundaries
C. To enhance colors
D. To detect motion
Answer: B
Explanation: Erosion shrinks the foreground objects in a binary image by removing boundary pixels, which helps in noise removal and shape simplification.
15. Which loss function is commonly used for binary image segmentation?
A. Cross-entropy loss
B. Mean squared error
C. Dice loss
D. Hinge loss
Answer: C
Explanation: Dice loss measures the overlap between predicted and actual segments, making it suitable for imbalanced datasets in binary segmentation tasks.
16. What is the primary function of a camera calibration process?
A. To adjust image colors
B. To estimate intrinsic and extrinsic parameters of the camera
C. To compress video streams
D. To apply filters
Answer: B
Explanation: Camera calibration determines parameters like focal length and distortion coefficients, which are essential for accurate 3D reconstruction from images.
17. Which technique is used for stereo vision to estimate depth?
A. Edge detection
B. Disparity mapping
C. Histogram equalization
D. Gaussian blurring
Answer: B
Explanation: Disparity mapping calculates the difference in object positions between two images from stereo cameras, allowing depth estimation based on triangulation.
18. What is the role of activation functions in CNNs?
A. To introduce non-linearity into the model
B. To increase image resolution
C. To store data
D. To reduce training time
Answer: A
Explanation: Functions like ReLU add non-linearity to the network, enabling it to learn complex patterns and avoid the limitations of linear models.
19. In object tracking, what does Kalman filter do?
A. Predicts the future state of objects based on previous states
B. Detects new objects in real-time
C. Enhances image quality
D. Classifies object types
Answer: A
Explanation: The Kalman filter uses a predictive model to estimate the position and velocity of objects over time, handling noise and uncertainty in tracking.
20. Which method is used for image super-resolution?
A. Bicubic interpolation
B. Edge detection
C. Optical flow
D. Histogram equalization
Answer: A
Explanation: Bicubic interpolation upsamples low-resolution images by estimating pixel values based on neighboring pixels, improving overall image quality.
or
Part 3: AI Question Generator – Automatically create questions for your next assessment
Automatically generate questions using AI