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 study of how human vision works.
C) The process of filtering and enhancing visual images.
D) The use of computer screens to display images.

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) Randomly distorting images.
D) Blurring images for artistic effect.

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

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

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

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

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

A) Using smaller batch sizes
B) Increasing the learning rate
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) Transferring gradients during backpropagation.
B) Transferring image pixels to a new image.
C) Using pre-trained models and fine-tuning for a specific task.
D) Transferring images between different devices.

7. What is the purpose of a 'pooling layer' in a convolutional neural network?

A) Normalizing input values.
B) Increasing the number of parameters.
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) Tanh
B) ReLU (Rectified Linear Unit)
C) Linear
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) 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) Spam dataset
B) Song lyrics dataset
C) Weather dataset
D) ImageNet

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

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

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

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

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

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

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

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

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

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

16. What does CNN stand for?

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

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

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

18. 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

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

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

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

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

21. 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

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

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

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

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

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