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