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 study of how human vision works.
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 use of computer screens to display images.

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

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

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) R-squared
C) Accuracy
D) Mean Squared Error

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) Using smaller batch sizes
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 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) Introducing non-linearity to the network.
D) Reducing the spatial dimensions of the input.

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

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

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

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

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

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

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

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

14. What does CNN stand for?

A) Controlled Neural Network
B) Complex Neuron 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) Softmax
B) ReLU
C) Sigmoid
D) Tanh

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

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

18. What does the term 'SIFT' stand for in the context of image recognition?

A) Segmentation of Image Features and Textures
B) Selective Image Filtering Technique
C) Semi-Integrated Face Tracking
D) Scale-Invariant Feature Transform

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

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

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

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

21. Which method can be used for computing optical flow in video processing?

A) Histogram equalization
B) Gaussian blur
C) Lucas-Kanade method
D) Fourier transform

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

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

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

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

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