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 field of study that enables computers to interpret and understand visual information from the real world.
B) The use of computer screens to display images.
C) The process of filtering and enhancing visual images.
D) The study of how human vision works.

2. What is the purpose of pre-processing images in Computer Vision?

A) Changing the image dimensions.
B) Blurring images for artistic effect.
C) Randomly distorting images.
D) Enhancing image quality and reducing noise for better analysis.

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

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

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

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

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

A) Adding more layers to the network
B) Using smaller batch sizes
C) Increasing the learning rate
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) Introducing non-linearity to the network.
B) Reducing the spatial dimensions of the input.
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) Linear
D) ReLU (Rectified Linear Unit)

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

A) Creating composite images.
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) Converting images to grayscale.

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

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

11. What is 'instance segmentation' in the context of object detection?

A) Smoothing pixel intensities.
B) Applying color filters to images.
C) Identifying and delineating individual objects within a scene.
D) Converting images to black and white.

12. 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

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

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

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

A) Increasing image resolution
B) Rotating images
C) Adding noise to images
D) Non-local means denoising

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

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

16. What does CNN stand for?

A) Computerized Neuron Network
B) Complex Neuron Network
C) Controlled Neural Network
D) Convolutional Neural Network

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

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

18. Which loss function is commonly used in image classification tasks?

A) Cross-Entropy Loss
B) Mean Squared Error
C) Binary Cross-Entropy Loss
D) L1 Loss

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

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

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

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

21. 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

22. Which activation function is commonly used in the output layer of a CNN for multi-class classification?

A) Softmax
B) ReLU
C) Sigmoid
D) Tanh

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

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

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