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