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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 field of study that enables computers to interpret and understand visual information from the real world.
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) Randomly distorting images.
B) Changing the image dimensions.
C) Enhancing image quality and reducing noise for better analysis.
D) Blurring images for artistic effect.
  • 3. What is meant by the term 'Image Segmentation'?
A) Creating a mirror image of the original.
B) Removing colors from an image.
C) Combining multiple images into one.
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) R-squared
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) 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) Using pre-trained models and fine-tuning for a specific task.
B) Transferring gradients during backpropagation.
C) Transferring images between different devices.
D) Transferring image pixels to a new image.
  • 7. What is the purpose of a 'pooling layer' in a convolutional neural network?
A) Increasing the number of parameters.
B) Normalizing input values.
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) ReLU (Rectified Linear Unit)
B) Linear
C) Tanh
D) Sigmoid
  • 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) Weather dataset
B) Song lyrics dataset
C) ImageNet
D) Spam dataset
  • 11. What is 'instance segmentation' in the context of object detection?
A) Smoothing pixel intensities.
B) Converting images to black and white.
C) Applying color filters to images.
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) Blurring image boundaries.
D) Mapping one image onto another image plane.
  • 13. Which method can be used for computing optical flow in video processing?
A) Gaussian blur
B) Fourier transform
C) Lucas-Kanade method
D) Histogram equalization
  • 14. Which pre-trained CNN model is commonly used for various image recognition tasks?
A) VGGNet
B) ResNet (Residual Network)
C) AlexNet
D) InceptionNet
  • 15. Which loss function is commonly used in image classification tasks?
A) Cross-Entropy Loss
B) L1 Loss
C) Mean Squared Error
D) Binary Cross-Entropy Loss
  • 16. Which activation function is commonly used in the output layer of a CNN for multi-class classification?
A) Softmax
B) Sigmoid
C) Tanh
D) ReLU
  • 17. 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
  • 18. 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)
  • 19. 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
  • 20. What does CNN stand for?
A) Complex Neuron Network
B) Controlled Neural Network
C) Computerized Neuron Network
D) Convolutional Neural Network
  • 21. Which layer in a CNN is responsible for reducing spatial dimensions?
A) Pooling layer
B) Convolutional layer
C) Fully connected layer
D) Activation layer
  • 22. Which technique can be used for fine-tuning a pre-trained CNN model for a new task?
A) Image Cropping
B) Transfer Learning
C) Noise Injection
D) PCA Dimensionality Reduction
  • 23. Which technique is used to identify and locate objects within an image?
A) Object detection
B) Image segmentation
C) Image classification
D) Feature extraction
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