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