Advanced Computer Vision MCQs

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1. What is “object detection” in computer vision? A) Identifying and locating objects within an image B) Recognizing text in images C) Determining the color of an object D) Classifying images into categories Answer: A) Identifying and locating objects within an image 2. Which algorithm is commonly used for object detection? A) YOLO (You Only Look Once) B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) YOLO (You Only Look Once) 3. What is the primary purpose of a Convolutional Neural Network (CNN) in computer vision? A) Extracting features from images B) Performing data clustering C) Generating random numbers D) Performing linear regression Answer: A) Extracting features from images 4. Which network architecture is known for its use in image classification tasks? A) VGGNet B) Recurrent Neural Network C) Long Short-Term Memory D) Generative Adversarial Network Answer: A) VGGNet 5. What does “image segmentation” involve in computer vision? A) Dividing an image into segments to simplify its analysis B) Detecting edges in an image C) Recognizing faces in an image D) Enhancing image quality Answer: A) Dividing an image into segments to simplify its analysis 6. Which technique is commonly used for semantic segmentation? A) Fully Convolutional Networks (FCNs) B) K-means clustering C) Principal Component Analysis D) Random Forest Answer: A) Fully Convolutional Networks (FCNs) 7. What is the main advantage of using a Generative Adversarial Network (GAN) in computer vision? A) Generating realistic images from noise B) Performing image classification C) Detecting edges in images D) Clustering image data Answer: A) Generating realistic images from noise 8. What is “image registration” in the context of computer vision? A) Aligning two or more images to achieve spatial correspondence B) Classifying images into categories C) Detecting objects within an image D) Segmenting images into regions Answer: A) Aligning two or more images to achieve spatial correspondence 9. Which of the following is a common technique for image enhancement? A) Histogram Equalization B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) Histogram Equalization 10. What is “feature extraction” in computer vision? A) The process of identifying and extracting relevant features from an image B) Clustering similar images C) Detecting edges in images D) Generating random images Answer: A) The process of identifying and extracting relevant features from an image 11. What is the primary use of “optical flow” in computer vision? A) Estimating motion between two image frames B) Classifying images C) Generating new images D) Detecting edges in images Answer: A) Estimating motion between two image frames 12. Which algorithm is used for detecting corners in an image? A) Harris Corner Detection B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) Harris Corner Detection 13. What is “depth estimation” in computer vision? A) Determining the distance of objects from the camera B) Identifying objects in an image C) Enhancing image quality D) Recognizing text in images Answer: A) Determining the distance of objects from the camera 14. Which method is commonly used for face recognition? A) Eigenfaces B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) Eigenfaces 15. What is “object tracking” in computer vision? A) Following the movement of an object across frames in a video B) Identifying objects in a single frame C) Enhancing image quality D) Segmenting images into regions Answer: A) Following the movement of an object across frames in a video 16. Which technique is used to reduce the dimensionality of image data? A) Principal Component Analysis (PCA) B) K-means clustering C) Convolutional Neural Networks D) Histogram Equalization Answer: A) Principal Component Analysis (PCA) 17. What does “image denoising” refer to? A) Removing noise and enhancing the quality of an image B) Detecting objects in an image C) Clustering similar images D) Generating random images Answer: A) Removing noise and enhancing the quality of an image 18. Which of the following is a key challenge in “video analysis”? A) Handling temporal changes and motion in videos B) Enhancing image quality C) Performing image segmentation D) Clustering image data Answer: A) Handling temporal changes and motion in videos 19. What is “image stitching” in computer vision? A) Combining multiple images to create a panoramic image B) Detecting edges in images C) Recognizing faces in images D) Generating new images Answer: A) Combining multiple images to create a panoramic image 20. Which approach is commonly used for image classification tasks? A) Convolutional Neural Networks (CNNs) B) K-means clustering C) Support Vector Machines D) Principal Component Analysis Answer: A) Convolutional Neural Networks (CNNs) 21. What does “image captioning” involve in computer vision? A) Generating descriptive text for an image B) Identifying objects in an image C) Enhancing image quality D) Detecting edges in images Answer: A) Generating descriptive text for an image 22. What is “semantic segmentation”? A) Dividing an image into regions with meaningful labels B) Detecting edges in an image C) Recognizing faces in an image D) Performing image classification Answer: A) Dividing an image into regions with meaningful labels 23. What does “instance segmentation” refer to? A) Segmenting each object instance in an image individually B) Identifying objects in an image C) Enhancing image quality D) Detecting edges in images Answer: A) Segmenting each object instance in an image individually 24. Which of the following methods is used for object recognition in images? A) Region-based Convolutional Neural Networks (R-CNNs) B) K-means clustering C) Principal Component Analysis D) Histogram Equalization Answer: A) Region-based Convolutional Neural Networks (R-CNNs) 25. What is “action recognition” in video analysis? A) Identifying and classifying actions or activities occurring in video sequences B) Enhancing image quality C) Detecting objects in images D) Segmenting images into regions Answer: A) Identifying and classifying actions or activities occurring in video sequences 26. Which technique is used for object localization in images? A) Bounding box regression B) Principal Component Analysis C) K-means clustering D) Histogram Equalization Answer: A) Bounding box regression 27. What does “image super-resolution” refer to? A) Enhancing the resolution and quality of an image B) Detecting edges in images C) Clustering similar images D) Generating random images Answer: A) Enhancing the resolution and quality of an image 28. What is “depth sensing” in computer vision? A) Measuring the distance of objects from the camera using depth sensors B) Identifying objects in an image C) Enhancing image quality D) Recognizing text in images Answer: A) Measuring the distance of objects from the camera using depth sensors 29. What does “image retrieval” involve? A) Searching for and retrieving images from a database based on query images B) Enhancing image quality C) Detecting edges in images D) Generating new images Answer: A) Searching for and retrieving images from a database based on query images 30. Which method is commonly used for “face detection”? A) Haar Cascades B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) Haar Cascades 31. What is “spatial transformation” in computer vision? A) Applying transformations to spatial coordinates in images, such as rotation and scaling B) Detecting objects in an image C) Enhancing image quality D) Clustering similar images Answer: A) Applying transformations to spatial coordinates in images, such as rotation and scaling 32. What does “image enhancement” aim to achieve? A) Improving the quality and visual appearance of an image B) Detecting objects in an image C) Performing image classification D) Generating random images Answer: A) Improving the quality and visual appearance of an image 33. Which algorithm is used for “image segmentation” using deep learning? A) U-Net B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) U-Net 34. What is “object recognition” in computer vision? A) Identifying and classifying objects within an image B) Generating new images C) Detecting edges in images D) Enhancing image quality Answer: A) Identifying and classifying objects within an image 35. What does “action detection” involve in video analysis? A) Identifying and locating actions occurring in video frames B) Enhancing image quality C) Recognizing faces in images D) Clustering image data Answer: A) Identifying and locating actions occurring in video frames 36. Which technique is used for “object segmentation”? A) Mask R-CNN B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) Mask R-CNN 37. What is “feature matching” in computer vision? A) Identifying corresponding features between different images B) Detecting objects in an image C) Enhancing image quality D) Generating random images Answer: A) Identifying corresponding features between different images 38. What does “scene understanding” involve in computer vision? A) Interpreting and understanding the content and context of a scene in an image B) Detecting edges in images C) Performing image classification D) Generating new images Answer: A) Interpreting and understanding the content and context of a scene in an image 39. What is “image classification” in computer vision? A) Categorizing images into predefined classes or labels B) Detecting objects in an image C) Enhancing image quality D) Generating random images Answer: A) Categorizing images into predefined classes or labels 40. Which network is used for “semantic segmentation”? A) U-Net B) K-means clustering C) Principal Component Analysis D) Support Vector Machine Answer: A) U-Net

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