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Computer Vision and Image Recognition
  • 1. Computer vision is an interdisciplinary field that enables computers to interpret and understand the visual world from digital images or videos. It involves the development of algorithms and techniques to extract meaningful information from visual data, mimicking the human visual system's capabilities. Image recognition, a subset of computer vision, focuses on identifying and categorizing objects, scenes, or patterns in images or videos. Through the use of deep learning, neural networks, and machine learning, computer vision and image recognition have applications in various domains, including healthcare, autonomous vehicles, surveillance, augmented reality, and more.

    What is Computer Vision?
A) The use of computer screens to display images.
B) The study of how human vision works.
C) The field of study that enables computers to interpret and understand visual information from the real world.
D) The process of filtering and enhancing visual images.
  • 2. What is the purpose of pre-processing images in Computer Vision?
A) Blurring images for artistic effect.
B) Changing the image dimensions.
C) Randomly distorting images.
D) Enhancing image quality and reducing noise for better analysis.
  • 3. What is meant by the term 'Image Segmentation'?
A) Dividing an image into meaningful regions or objects for analysis.
B) Combining multiple images into one.
C) Creating a mirror image of the original.
D) Removing colors from an image.
  • 4. Which evaluation metric is commonly used for image classification tasks?
A) Accuracy
B) F1 Score
C) R-squared
D) Mean Squared Error
  • 5. Which technique can be used to reduce overfitting in deep learning models for image recognition?
A) Using smaller batch sizes
B) Dropout regularization
C) Increasing the learning rate
D) Adding more layers to the network
  • 6. What is meant by 'transfer learning' in the context of deep learning for image recognition?
A) Transferring images between different devices.
B) Using pre-trained models and fine-tuning for a specific task.
C) Transferring gradients during backpropagation.
D) Transferring image pixels to a new image.
  • 7. What is the purpose of a 'pooling layer' in a convolutional neural network?
A) Introducing non-linearity to the network.
B) Normalizing input values.
C) Increasing the number of parameters.
D) Reducing the spatial dimensions of the input.
  • 8. Which activation function is commonly used in convolutional neural networks?
A) ReLU (Rectified Linear Unit)
B) Tanh
C) Sigmoid
D) Linear
  • 9. What is a 'confusion matrix' used for in evaluating image classification models?
A) Summarizing the performance of a classification model using true positive, false positive, true negative, and false negative values.
B) Creating composite images.
C) Converting images to grayscale.
D) Blurring images for privacy protection.
  • 10. Which technique can be used for fine-tuning a pre-trained CNN model for a new task?
A) Image Cropping
B) Transfer Learning
C) PCA Dimensionality Reduction
D) Noise Injection
  • 11. What is the purpose of homography in Computer Vision?
A) Mapping one image onto another image plane.
B) Detecting object edges.
C) Blurring image boundaries.
D) Normalizing image histograms.
  • 12. Which technique is used to identify and locate objects within an image?
A) Image segmentation
B) Image classification
C) Object detection
D) Feature extraction
  • 13. Which technique is used for image denoising in Computer Vision?
A) Increasing image resolution
B) Rotating images
C) Non-local means denoising
D) Adding noise to images
  • 14. What does CNN stand for?
A) Controlled Neural Network
B) Computerized Neuron Network
C) Convolutional Neural Network
D) Complex Neuron Network
  • 15. Which activation function is commonly used in the output layer of a CNN for multi-class classification?
A) Softmax
B) ReLU
C) Tanh
D) Sigmoid
  • 16. Which loss function is commonly used in image classification tasks?
A) L1 Loss
B) Binary Cross-Entropy Loss
C) Cross-Entropy Loss
D) Mean Squared Error
  • 17. Which pre-trained CNN model is commonly used for various image recognition tasks?
A) AlexNet
B) InceptionNet
C) ResNet (Residual Network)
D) VGGNet
  • 18. What does the term 'SIFT' stand for in the context of image recognition?
A) Semi-Integrated Face Tracking
B) Segmentation of Image Features and Textures
C) Scale-Invariant Feature Transform
D) Selective Image Filtering Technique
  • 19. Which technique is commonly used for image feature extraction?
A) Support Vector Machines (SVM)
B) Convolutional Neural Networks (CNNs)
C) Principal Component Analysis (PCA)
D) K-Nearest Neighbors (KNN)
  • 20. What is 'instance segmentation' in the context of object detection?
A) Identifying and delineating individual objects within a scene.
B) Converting images to black and white.
C) Applying color filters to images.
D) Smoothing pixel intensities.
  • 21. Which method can be used for computing optical flow in video processing?
A) Histogram equalization
B) Fourier transform
C) Gaussian blur
D) Lucas-Kanade method
  • 22. Which is an example of a popular dataset commonly used for image recognition tasks?
A) Spam dataset
B) Weather dataset
C) ImageNet
D) Song lyrics dataset
  • 23. Which layer in a CNN is responsible for reducing spatial dimensions?
A) Pooling layer
B) Activation layer
C) Convolutional layer
D) Fully connected layer
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