Computer Vision and Image Recognition

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 use of computer screens to display images.
B) The process of filtering and enhancing visual images.
C) The field of study that enables computers to interpret and understand visual information from the real world.
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) Changing the image dimensions.
C) Blurring images for artistic effect.
D) Randomly distorting images.

3. What is meant by the term 'Image Segmentation'?

A) Removing colors from an image.
B) Creating a mirror image of the original.
C) Dividing an image into meaningful regions or objects for analysis.
D) Combining multiple images into one.

4. Which evaluation metric is commonly used for image classification tasks?

A) R-squared
B) F1 Score
C) Accuracy
D) Mean Squared Error

5. Which technique can be used to reduce overfitting in deep learning models for image recognition?

A) Increasing the learning rate
B) Dropout regularization
C) Adding more layers to the network
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) Using pre-trained models and fine-tuning for a specific task.
C) Transferring gradients during backpropagation.
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) Reducing the spatial dimensions of the input.
C) Introducing non-linearity to the network.
D) Normalizing input values.

8. Which activation function is commonly used in convolutional neural networks?

A) Linear
B) Tanh
C) Sigmoid
D) ReLU (Rectified Linear Unit)

9. What is a 'confusion matrix' used for in evaluating image classification models?

A) Converting images to grayscale.
B) Blurring images for privacy protection.
C) Summarizing the performance of a classification model using true positive, false positive, true negative, and false negative values.
D) Creating composite images.

10. Which technique can be used for fine-tuning a pre-trained CNN model for a new task?

A) Transfer Learning
B) Noise Injection
C) Image Cropping
D) PCA Dimensionality Reduction

11. What is the purpose of homography in Computer Vision?

A) Normalizing image histograms.
B) Blurring image boundaries.
C) Mapping one image onto another image plane.
D) Detecting object edges.

12. Which technique is used to identify and locate objects within an image?

A) Feature extraction
B) Object detection
C) Image classification
D) Image segmentation

13. Which technique is used for image denoising in Computer Vision?

A) Adding noise to images
B) Increasing image resolution
C) Rotating images
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) Softmax
C) Tanh
D) ReLU

16. 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

17. Which pre-trained CNN model is commonly used for various image recognition tasks?

A) AlexNet
B) ResNet (Residual Network)
C) VGGNet
D) InceptionNet

18. 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) Selective Image Filtering Technique
D) Scale-Invariant Feature Transform

19. Which technique is commonly used for image feature extraction?

A) K-Nearest Neighbors (KNN)
B) Convolutional Neural Networks (CNNs)
C) Support Vector Machines (SVM)
D) Principal Component Analysis (PCA)

20. 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.

21. 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

22. Which is an example of a popular dataset commonly used for image recognition tasks?

A) ImageNet
B) Song lyrics dataset
C) Spam dataset
D) Weather dataset

23. Which layer in a CNN is responsible for reducing spatial dimensions?

A) Fully connected layer
B) Convolutional layer
C) Activation layer
D) Pooling layer

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