Computer Vision in AI: Understanding Image Recognition and Algorithms MCQs
Explore key concepts like object detection, neural networks and deep learning applications in AI. Ideal for students and AI professionals.
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1. What is the primary goal of computer vision in AI?
- To generate synthetic images
- To enable machines to interpret and understand visual data
- To replace all human vision capabilities
- To enhance audio recognition models
2. Which of the following is a common application of computer vision?
- Speech recognition
- Optical Character Recognition (OCR)
- Text summarization
- Machine translation
3. What is the purpose of image segmentation in computer vision?
- To split an image into meaningful parts for analysis
- To blur an image for artistic effects
- To reduce the size of an image
- To convert an image into text
4. Which algorithm is commonly used for face detection?
- R-CNN
- YOLO (You Only Look Once)
- Naïve Bayes
- K-Means Clustering
5. What type of neural network is most commonly used in computer vision tasks?
- Recurrent Neural Network (RNN)
- Generative Adversarial Network (GAN)
- Bayesian Network
- Convolutional Neural Network (CNN)
6. Which dataset is widely used for training image classification models?
- MNIST
- IMDB Reviews
- ImageNet
- COCO
7. What is an activation function commonly used in deep learning for image processing?
- Softmax
- ReLU (Rectified Linear Unit)
- Sigmoid
- Tanh
8. What is the main advantage of YOLO over R-CNN?
- YOLO is significantly faster in object detection
- YOLO provides higher accuracy in text recognition
- YOLO does not require a trained model
- YOLO works only on grayscale images
9. What technique is used to artificially increase the size of a dataset in image recognition?
- Dropout
- Data Augmentation
- Batch Normalization
- Gradient Descent
10. What is Transfer Learning in computer vision?
- Using pre-trained models to solve new tasks with minimal training
- Learning without labeled data
- A method of compressing image data
- A way to improve audio processing models
11. What is the role of edge detection in image processing?
- To detect the boundaries of objects within an image
- To enhance image resolution
- To change the color scheme of an image
- To compress image data
12. Which of the following is NOT an image classification architecture?
- ResNet
- VGGNet
- LeNet
- Word2Vec
13. What does COCO dataset stand for?
- Common Objects in Context
- Computer Object Classification and Optimization
- Convolutional Object Categorization Output
- Complex Overlapping Computer Objects
14. What is the main challenge in object detection?
- Recognizing multiple objects in different scales and orientations
- Understanding spoken language
- Translating text to different languages
- Storing high-resolution images
15. What type of problem does Optical Character Recognition (OCR) solve?
- Speech-to-text conversion
- Identifying text within images
- Image compression
- Face recognition
16. Which of these architectures is commonly used for real-time object detection?
- Fast R-CNN
- YOLO
- AlexNet
- GPT-3
17. What is the term for generating images using AI models?
- Image Synthesis
- Optical Recognition
- Image Compression
- Image Clustering
18. How does CNN differ from traditional neural networks for images?
- CNN has no hidden layers
- CNN does not require training data
- CNN is used only for text processing
- CNN uses convolutional layers to extract spatial features
19. What is the function of Batch Normalization in deep learning?
- It speeds up training and stabilizes the learning process
- It reduces the number of parameters in a model
- It increases the dataset size
- It converts an image into text
20. Which deep learning framework is commonly used for computer vision?
- TensorFlow
- PyTorch
- OpenCV
- All of the above
21. What is the main advantage of ResNet architecture?
- It solves the vanishing gradient problem using residual connections
- It reduces model training time to zero
- It does not use convolutional layers
- It is only used for NLP
22. What is a bounding box in object detection?
- A file format for storing images
- A method of encrypting images
- A rectangular box that encloses an object in an image
- A way to remove background noise
23. What is a heatmap in computer vision?
- A visual representation of areas of interest in an image
- A temperature measurement tool
- A method of increasing image brightness
- A way to classify grayscale images
24. Which of these is a loss function commonly used in image classification?
- Mean Squared Error
- Cross-Entropy Loss
- BLEU Score
- Jaccard Index
25. What is the purpose of feature extraction in image recognition?
- To remove noise from an image
- To change the resolution of an image
- To store images more efficiently
- To identify and represent important patterns in an image
26. What is the role of a fully connected layer in a CNN?
- To apply convolution operations to an image
- To reduce the size of an image
- To map extracted features to the final output classification
- To perform edge detection
27. What is one drawback of deep learning models in image recognition?
- They require large amounts of labeled data for training
- They cannot process grayscale images
- They always misclassify objects
- They do not support real-time processing
28. Which of these is an advanced architecture for handling sequential image data?
- LSTM (Long Short-Term Memory)
- CNN (Convolutional Neural Network)
- GAN (Generative Adversarial Network)
- R-CNN (Region-Based Convolutional Neural Network)
29. What does the term "overfitting" mean in computer vision models?
- The model performs better on new data than on training data
- The model memorizes training data but fails to generalize well to new images
- The model does not learn any patterns
- The model ignores high-resolution images
30. What is a key function of GANs in computer vision?
- To classify images into categories
- To detect objects in real-time
- To perform edge detection
- To generate realistic synthetic images