Computer Vision in AI: Understanding Image Recognition and Algorithms MCQs

Questions (30)

1. What is the primary goal of computer vision in AI?

   • a) To generate synthetic images
   • b) To enable machines to interpret and understand visual data
   • c) To replace all human vision capabilities
   • d) To enhance audio recognition models
   View Answer

   Correct To enable machines to interpret and understand visual data

2. Which of the following is a common application of computer vision?

   • a) Speech recognition
   • b) Optical Character Recognition (OCR)
   • c) Text summarization
   • d) Machine translation
   View Answer

   Correct Optical Character Recognition (OCR)

3. What is the purpose of image segmentation in computer vision?

   • a) To split an image into meaningful parts for analysis
   • b) To blur an image for artistic effects
   • c) To reduce the size of an image
   • d) To convert an image into text
   View Answer

   Correct To split an image into meaningful parts for analysis

4. Which algorithm is commonly used for face detection?

   • a) R-CNN
   • b) YOLO (You Only Look Once)
   • c) Naïve Bayes
   • d) K-Means Clustering
   View Answer

   Correct YOLO (You Only Look Once)

5. What type of neural network is most commonly used in computer vision tasks?

   • a) Recurrent Neural Network (RNN)
   • b) Generative Adversarial Network (GAN)
   • c) Bayesian Network
   • d) Convolutional Neural Network (CNN)
   View Answer

   Correct Convolutional Neural Network (CNN)

6. Which dataset is widely used for training image classification models?

   • a) MNIST
   • b) IMDB Reviews
   • c) ImageNet
   • d) COCO
   View Answer

   Correct ImageNet

7. What is an activation function commonly used in deep learning for image processing?

   • a) Softmax
   • b) ReLU (Rectified Linear Unit)
   • c) Sigmoid
   • d) Tanh
   View Answer

   Correct ReLU (Rectified Linear Unit)

8. What is the main advantage of YOLO over R-CNN?

   • a) YOLO is significantly faster in object detection
   • b) YOLO provides higher accuracy in text recognition
   • c) YOLO does not require a trained model
   • d) YOLO works only on grayscale images
   View Answer

   Correct YOLO is significantly faster in object detection

9. What technique is used to artificially increase the size of a dataset in image recognition?

   • a) Dropout
   • b) Data Augmentation
   • c) Batch Normalization
   • d) Gradient Descent
   View Answer

   Correct Data Augmentation

10. What is Transfer Learning in computer vision?

    • a) Using pre-trained models to solve new tasks with minimal training
    • b) Learning without labeled data
    • c) A method of compressing image data
    • d) A way to improve audio processing models
    View Answer

    Correct Using pre-trained models to solve new tasks with minimal training

11. What is the role of edge detection in image processing?

    • a) To detect the boundaries of objects within an image
    • b) To enhance image resolution
    • c) To change the color scheme of an image
    • d) To compress image data
    View Answer

    Correct To detect the boundaries of objects within an image

12. Which of the following is NOT an image classification architecture?

    • a) ResNet
    • b) VGGNet
    • c) LeNet
    • d) Word2Vec
    View Answer

    Correct Word2Vec

13. What does COCO dataset stand for?

    • a) Common Objects in Context
    • b) Computer Object Classification and Optimization
    • c) Convolutional Object Categorization Output
    • d) Complex Overlapping Computer Objects
    View Answer

    Correct Common Objects in Context

14. What is the main challenge in object detection?

    • a) Recognizing multiple objects in different scales and orientations
    • b) Understanding spoken language
    • c) Translating text to different languages
    • d) Storing high-resolution images
    View Answer

    Correct Recognizing multiple objects in different scales and orientations

15. What type of problem does Optical Character Recognition (OCR) solve?

    • a) Speech-to-text conversion
    • b) Identifying text within images
    • c) Image compression
    • d) Face recognition
    View Answer

    Correct Identifying text within images

16. Which of these architectures is commonly used for real-time object detection?

    • a) Fast R-CNN
    • b) YOLO
    • c) AlexNet
    • d) GPT-3
    View Answer

    Correct YOLO

17. What is the term for generating images using AI models?

    • a) Image Synthesis
    • b) Optical Recognition
    • c) Image Compression
    • d) Image Clustering
    View Answer

    Correct Image Synthesis

18. How does CNN differ from traditional neural networks for images?

    • a) CNN has no hidden layers
    • b) CNN does not require training data
    • c) CNN is used only for text processing
    • d) CNN uses convolutional layers to extract spatial features
    View Answer

    Correct CNN uses convolutional layers to extract spatial features

19. What is the function of Batch Normalization in deep learning?

    • a) It speeds up training and stabilizes the learning process
    • b) It reduces the number of parameters in a model
    • c) It increases the dataset size
    • d) It converts an image into text
    View Answer

    Correct It speeds up training and stabilizes the learning process

20. Which deep learning framework is commonly used for computer vision?

    • a) TensorFlow
    • b) PyTorch
    • c) OpenCV
    • d) All of the above
    View Answer

    Correct All of the above

21. What is the main advantage of ResNet architecture?

    • a) It solves the vanishing gradient problem using residual connections
    • b) It reduces model training time to zero
    • c) It does not use convolutional layers
    • d) It is only used for NLP
    View Answer

    Correct It solves the vanishing gradient problem using residual connections

22. What is a bounding box in object detection?

    • a) A file format for storing images
    • b) A method of encrypting images
    • c) A rectangular box that encloses an object in an image
    • d) A way to remove background noise
    View Answer

    Correct A rectangular box that encloses an object in an image

23. What is a heatmap in computer vision?

    • a) A visual representation of areas of interest in an image
    • b) A temperature measurement tool
    • c) A method of increasing image brightness
    • d) A way to classify grayscale images
    View Answer

    Correct A visual representation of areas of interest in an image

24. Which of these is a loss function commonly used in image classification?

    • a) Mean Squared Error
    • b) Cross-Entropy Loss
    • c) BLEU Score
    • d) Jaccard Index
    View Answer

    Correct Cross-Entropy Loss

25. What is the purpose of feature extraction in image recognition?

    • a) To remove noise from an image
    • b) To change the resolution of an image
    • c) To store images more efficiently
    • d) To identify and represent important patterns in an image
    View Answer

    Correct To identify and represent important patterns in an image

26. What is the role of a fully connected layer in a CNN?

    • a) To apply convolution operations to an image
    • b) To reduce the size of an image
    • c) To map extracted features to the final output classification
    • d) To perform edge detection
    View Answer

    Correct To map extracted features to the final output classification

27. What is one drawback of deep learning models in image recognition?

    • a) They require large amounts of labeled data for training
    • b) They cannot process grayscale images
    • c) They always misclassify objects
    • d) They do not support real-time processing
    View Answer

    Correct They require large amounts of labeled data for training

28. Which of these is an advanced architecture for handling sequential image data?

    • a) LSTM (Long Short-Term Memory)
    • b) CNN (Convolutional Neural Network)
    • c) GAN (Generative Adversarial Network)
    • d) R-CNN (Region-Based Convolutional Neural Network)
    View Answer

    Correct LSTM (Long Short-Term Memory)

29. What does the term "overfitting" mean in computer vision models?

    • a) The model performs better on new data than on training data
    • b) The model memorizes training data but fails to generalize well to new images
    • c) The model does not learn any patterns
    • d) The model ignores high-resolution images
    View Answer

    Correct The model memorizes training data but fails to generalize well to new images

30. What is a key function of GANs in computer vision?

    • a) To classify images into categories
    • b) To detect objects in real-time
    • c) To perform edge detection
    • d) To generate realistic synthetic images
    View Answer

    Correct To generate realistic synthetic images