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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 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 use of computer screens to display images.
D) The study of how human vision works.
  • 2. What is the purpose of pre-processing images in Computer Vision?
A) Enhancing image quality and reducing noise for better analysis.
B) Randomly distorting images.
C) Changing the image dimensions.
D) Blurring images for artistic effect.
  • 3. What is meant by the term 'Image Segmentation'?
A) Dividing an image into meaningful regions or objects for analysis.
B) Creating a mirror image of the original.
C) Combining multiple images into one.
D) Removing colors from an image.
  • 4. Which evaluation metric is commonly used for image classification tasks?
A) Accuracy
B) F1 Score
C) Mean Squared Error
D) R-squared
  • 5. Which technique can be used to reduce overfitting in deep learning models for image recognition?
A) Dropout regularization
B) Increasing the learning rate
C) Using smaller batch sizes
D) Adding more layers to the network
  • 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 gradients during backpropagation.
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) Linear
B) Tanh
C) ReLU (Rectified Linear Unit)
D) Sigmoid
  • 9. What is a 'confusion matrix' used for in evaluating image classification models?
A) Creating composite images.
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) Blurring images for privacy protection.
  • 10. Which is an example of a popular dataset commonly used for image recognition tasks?
A) ImageNet
B) Weather dataset
C) Spam dataset
D) Song lyrics dataset
  • 11. 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.
  • 12. What is the purpose of homography in Computer Vision?
A) Detecting object edges.
B) Normalizing image histograms.
C) Mapping one image onto another image plane.
D) Blurring image boundaries.
  • 13. Which method can be used for computing optical flow in video processing?
A) Lucas-Kanade method
B) Gaussian blur
C) Histogram equalization
D) Fourier transform
  • 14. Which pre-trained CNN model is commonly used for various image recognition tasks?
A) ResNet (Residual Network)
B) InceptionNet
C) AlexNet
D) VGGNet
  • 15. Which loss function is commonly used in image classification tasks?
A) L1 Loss
B) Cross-Entropy Loss
C) Binary Cross-Entropy Loss
D) Mean Squared Error
  • 16. Which activation function is commonly used in the output layer of a CNN for multi-class classification?
A) Tanh
B) Softmax
C) ReLU
D) Sigmoid
  • 17. Which technique is used for image denoising in Computer Vision?
A) Increasing image resolution
B) Adding noise to images
C) Rotating images
D) Non-local means denoising
  • 18. Which technique is commonly used for image feature extraction?
A) Support Vector Machines (SVM)
B) Principal Component Analysis (PCA)
C) Convolutional Neural Networks (CNNs)
D) K-Nearest Neighbors (KNN)
  • 19. What does the term 'SIFT' stand for in the context of image recognition?
A) Selective Image Filtering Technique
B) Semi-Integrated Face Tracking
C) Segmentation of Image Features and Textures
D) Scale-Invariant Feature Transform
  • 20. What does CNN stand for?
A) Computerized Neuron Network
B) Complex Neuron Network
C) Convolutional Neural Network
D) Controlled Neural Network
  • 21. Which layer in a CNN is responsible for reducing spatial dimensions?
A) Activation layer
B) Fully connected layer
C) Pooling layer
D) Convolutional layer
  • 22. 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
  • 23. Which technique is used to identify and locate objects within an image?
A) Object detection
B) Feature extraction
C) Image segmentation
D) Image classification
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