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 process of filtering and enhancing visual images.
B) The study of how human vision works.
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) Blurring images for artistic effect.
B) Changing the image dimensions.
C) Randomly distorting images.
D) Enhancing image quality and reducing noise for better analysis.

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

A) Creating a mirror image of the original.
B) Removing colors from an image.
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) Mean Squared Error
B) R-squared
C) Accuracy
D) F1 Score

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) Dropout regularization
D) Adding more layers to the network

6. What is meant by 'transfer learning' in the context of deep learning for image recognition?

A) Transferring image pixels to a new image.
B) Transferring gradients during backpropagation.
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) Increasing the number of parameters.
D) Introducing non-linearity to the network.

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

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

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

A) Blurring images for privacy protection.
B) Converting images to grayscale.
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 is an example of a popular dataset commonly used for image recognition tasks?

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

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

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

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

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

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

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

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

16. What does CNN stand for?

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

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

A) Activation layer
B) Pooling 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) Mean Squared Error
C) L1 Loss
D) Cross-Entropy Loss

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

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

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

A) Convolutional Neural Networks (CNNs)
B) Support Vector Machines (SVM)
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) Semi-Integrated Face Tracking
C) Selective Image Filtering Technique
D) Scale-Invariant Feature Transform

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

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

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

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

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