Computer Vision MCQs

1. What is computer vision?

a) The study of computer graphics

b) The study of computer networks

c) The study of algorithms for analyzing and interpreting visual data

d) The study of computer hardware architecture

Answer: c) The study of algorithms for analyzing and interpreting visual data

2. Which of the following is not a fundamental task in computer vision?

a) Object detection

b) Image classification

c) Speech recognition

d) Image segmentation

Answer: c) Speech recognition

3. Which technique is commonly used for edge detection in images?

a) Fourier transform

b) Histogram equalization

c) Canny edge detection

d) Gaussian blur

Answer: c) Canny edge detection

4. Which of the following algorithms is commonly used for image classification?

a) K-means clustering

b) Decision trees

c) Convolutional Neural Networks (CNN)

d) Support Vector Machines (SVM)

Answer: c) Convolutional Neural Networks (CNN)

5. Which concept is used to represent the transformation from a 3D scene to a 2D image?

a) Perspective projection

b) Affine transformation

c) Euler angles

d) Homography

Answer: a) Perspective projection

6. Which technique is used to detect and track objects in a sequence of frames?

a) Optical flow

b) Template matching

c) Harris corner detection

d) Scale-invariant feature transform (SIFT)

Answer: a) Optical flow

7. Which metric is commonly used to evaluate the performance of object detection algorithms?

a) Precision

b) Recall

c) F1 score

d) All of the above

Answer: d) All of the above

8. Which technique is used to remove noise from images while preserving important details?

a) Median filtering

b) Sobel operator

c) Histogram equalization

d) Laplacian of Gaussian (LoG) filter

Answer: a) Median filtering

9. Which algorithm is commonly used for image segmentation?

a) K-means clustering

b) Depth-first search

c) Gaussian blur

d) Fourier transform

Answer: a) K-means clustering

10. Which technique is used to recognize and understand text in images or videos?

a) Optical character recognition (OCR)

b) Motion detection

c) Feature extraction

d) Image stitching

Answer: a) Optical character recognition (OCR)

11. Which of the following is a popular pre-trained deep learning model for image classification?

a) AlexNet

b) K-means

c) Random Forests

d) Principal Component Analysis (PCA)

Answer: a) AlexNet

12. Which technique is used to estimate the camera's pose and reconstruct the 3D structure of a scene?

a) Depth estimation

b) Stereo vision

c) Structure from Motion (SfM)

d) Texture mapping

Answer: c) Structure from Motion (SfM)

13. Which concept is used to represent the local features of an image that are invariant to scale and rotation?

a) Hough transform

b) Histogram of Oriented Gradients (HOG)

c) Scale-invariant feature transform (SIFT)

d) Eigenfaces

Answer: c) Scale-invariant feature transform (SIFT)

14. Which technique is used to align two or more images to create a single panoramic image?

a) Image segmentation

b) Template matching

c) Image stitching

d) Image compression

Answer: c) Image stitching

15. Which method is used to generate realistic images by learning from a large dataset?

a) GANs (Generative Adversarial Networks)

b) Random Forests

c) K-means clustering

d) Principal Component Analysis (PCA)

Answer: a) GANs (Generative Adversarial Networks)

16. Which algorithm is commonly used for face recognition?

a) K-means clustering

b) Decision trees

c) Support Vector Machines (SVM)

d) DeepFace

Answer: d) DeepFace

17. Which technique is used to estimate the depth information of a scene from a single image?

a) Image segmentation

b) Depth from focus

c) Depth from motion

d) Structure from Motion (SfM)

Answer: b) Depth from focus

18. Which technique is used to recognize and track human body parts in images or videos?

a) Histogram of Oriented Gradients (HOG)

b) Optical flow

c) Haar cascades

d) SURF (Speeded-Up Robust Features)

Answer: c) Haar cascades

19. Which technique is used to generate a compact representation of an image by preserving its important features?

a) Histogram equalization

b) Principal Component Analysis (PCA)

c) Fourier transform

d) Median filtering

Answer: b) Principal Component Analysis (PCA)

20. Which algorithm is commonly used for object detection in images?

a) K-means clustering

b) Random Forests

c) Haar cascades

d) Support Vector Machines (SVM)

Answer: c) Haar cascades

21. Which technique is used to detect and track facial landmarks in images or videos?

a) Principal Component Analysis (PCA)

b) Optical flow

c) Active Shape Models (ASMs)

d) Histogram of Oriented Gradients (HOG)

Answer: c) Active Shape Models (ASMs)

22. Which method is used to represent colors in digital images?

a) RGB (Red, Green, Blue)

b) HSV (Hue, Saturation, Value)

c) CMYK (Cyan, Magenta, Yellow, Black)

d) All of the above

Answer: d) All of the above

23. Which technique is used to detect and recognize objects based on their geometric shapes?

a) Contour detection

b) Texture analysis

c) Blob detection

d) Edge detection

Answer: a) Contour detection

24. Which algorithm is commonly used for image registration, aligning two or more images?

a) K-means clustering

b) Optical flow

c) SURF (Speeded-Up Robust Features)

d) RANSAC (Random Sample Consensus)

Answer: d) RANSAC (Random Sample Consensus)

25. Which technique is used to classify images based on visual similarity?

a) Image retrieval

b) Semantic segmentation

c) Image captioning

d) Image synthesis

Answer: a) Image retrieval

26. Which concept is used to represent the geometric transformations of objects in an image?

a) Homography

b) Histogram equalization

c) Fourier transform

d) Canny edge detection

Answer: a) Homography

27. Which technique is used to remove the illumination variations in images?

a) Image sharpening

b) Histogram equalization

c) Histogram matching

d) Image denoising

Answer: c) Histogram matching

28. Which algorithm is commonly used for image super-resolution, enhancing the resolution of an image?

a) K-means clustering

b) Decision trees

c) Support Vector Machines (SVM)

d) Deep learning-based models

Answer: d) Deep learning-based models

29. Which technique is used to detect and recognize objects based on their texture patterns?

a) Edge detection

b) Histogram equalization

c) Texture analysis

d) Image compression

Answer: c) Texture analysis

30. Which concept is used to represent the 3D shape of an object using a set of 2D images?

a) Image alignment

b) Depth estimation

c) Shape from Shading

d) Structure from Motion (SfM)

Answer: d) Structure from Motion (SfM)