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