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)
  • NaΓ―ve 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