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 field of study that enables computers to interpret and understand visual information from the real world.
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) Enhancing image quality and reducing noise for better analysis.
B) Blurring images for artistic effect.
C) Changing the image dimensions.
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) Removing colors from an image.
C) Combining multiple images into one.
D) Creating a mirror image of the original.

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) Adding more layers to the network
C) Dropout regularization
D) Increasing the learning rate

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) Transferring images between different devices.
D) Using pre-trained models and fine-tuning for a specific task.

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

A) Normalizing input values.
B) Reducing the spatial dimensions of the input.
C) Introducing non-linearity to the network.
D) Increasing the number of parameters.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

16. What does CNN stand for?

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

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

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

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

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

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

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

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

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

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) Tanh
D) ReLU

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) Noise Injection
D) PCA Dimensionality Reduction

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