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