NSCT-Computer Vision MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Computer Vision (CV) is: (A) Backup only (B) Encrypting images only (C) Compressing datasets only (D) A field of AI that enables computers to interpret and process visual data such as images and videosShow All Answers 2. . Image preprocessing in computer vision includes: (A) Resizing, normalization, noise reduction, and color space conversion (B) Encrypting images (C) Compressing images (D) Backup only 3. . Convolutional Neural Networks (CNNs) are widely used in CV because they: (A) Automatically detect and learn spatial hierarchies of features (B) Encrypt image features (C) Compress image features (D) Backup only 4. . The convolutional layer in CNN performs: (A) Backup only (B) Encrypting layers (C) Compressing layers (D) Feature extraction using learnable filters or kernels 5. . Pooling layers in CNN help to: (A) Compress features (B) Encrypt pooled data (C) Reduce spatial dimensions and computational complexity while retaining important features (D) Backup only 6. . Fully connected layers in CNN are used for: (A) Combining extracted features to make predictions (B) Encrypting layers (C) Compressing layers (D) Backup only 7. . Image classification assigns: (A) Encrypting labels (B) A label to an input image from a predefined set of classes (C) Compressing labels (D) Backup only 8. . Object detection involves: (A) Backup only (B) Encrypting objects (C) Compressing objects (D) Identifying and locating objects within an image using bounding boxes 9. . Common object detection algorithms include: (A) YOLO, Faster R-CNN, SSD (B) Encrypting algorithms (C) Compressing algorithms (D) Backup only 10. . Semantic segmentation assigns: (A) Compressing pixels (B) Encrypting pixels (C) A class label to each pixel in the image (D) Backup only 11. . Instance segmentation differs from semantic segmentation because it: (A) Identifies each object instance separately in addition to class labels (B) Encrypts instances (C) Compresses instances (D) Backup only 12. . Edge detection in computer vision is used to: (A) Compress edges (B) Encrypt edges (C) Identify boundaries of objects within an image (D) Backup only 13. . Optical Character Recognition (OCR) is: (A) Encrypting text (B) Converting printed or handwritten text in images into machine-readable text (C) Compressing text (D) Backup only 14. . Image augmentation is performed to: (A) Increase dataset size and improve model generalization by applying transformations like rotation, flipping, and scaling (B) Encrypt images (C) Compress images (D) Backup only 15. . Feature maps in CNN represent: (A) Encrypting features (B) The output of convolutional layers highlighting important features (C) Compressing feature maps (D) Backup only 16. . Transfer learning in CV involves: (A) Encrypting models (B) Using pre-trained models on new tasks to reduce training time and improve performance (C) Compressing models (D) Backup only 17. . Common pre-trained models in CV include: (A) Compressing models (B) Encrypting models (C) VGGNet, ResNet, Inception, MobileNet (D) Backup only 18. . Convolution in CV helps to: (A) Backup only (B) Encrypt convolutions (C) Compress features (D) Detect features such as edges, textures, and patterns in images 19. . Object tracking is: (A) Encrypting objects (B) Following the movement of objects across video frames (C) Compressing objects (D) Backup only 20. . The main purpose of computer vision is to: (A) Enable machines to understand, interpret, and make decisions based on visual information (B) Encrypt all images (C) Compress datasets (D) Backup only