{"description":"Evaluate your understanding of image representation, preprocessing techniques, and feature extraction in machine vision.","questions":[{"answer":"As a 2D matrix of pixel intensities","number":1,"options":["As a string array","As a 3D tensor with RGB channels","As a 2D matrix of pixel intensities","As a binary tree"],"question":"How is a grayscale image typically represented in digital format?"},{"answer":"A combination of red, green, and blue intensities","number":2,"options":["An average value of all colors","The color name as a string","A combination of red, green, and blue intensities","Only grayscale brightness"],"question":"What does each pixel in a standard RGB color image represent?"},{"answer":"Normalization","number":3,"options":["Filtering","Resizing","Normalization","Augmentation"],"question":"Which preprocessing technique adjusts pixel values to a common scale like 0 to 1?"},{"answer":"To ensure all images have consistent dimensions","number":4,"options":["To improve image resolution","To ensure all images have consistent dimensions","To reduce the number of classes","To increase the dataset size"],"question":"Why is resizing images important before feeding them into a neural network?"},{"answer":"Translation","number":5,"options":["Translation","Resizing","Color balancing","Edge detection"],"question":"Which augmentation technique simulates objects appearing in different parts of an image?"},{"answer":"It increases model accuracy by adding variety","number":6,"options":["It removes noise from images","It compresses image files","It increases model accuracy by adding variety","It normalizes data to zero mean"],"question":"What is the main advantage of using data augmentation in image datasets?"},{"answer":"Convolutional Neural Network","number":7,"options":["Recurrent Neural Network","Fully Connected Network","Convolutional Neural Network","Bayesian Network"],"question":"What type of neural network is most suitable for extracting spatial features from images?"},{"answer":"Convolutional layer","number":8,"options":["Pooling layer","Dense layer","Dropout layer","Convolutional layer"],"question":"Which layer in a CNN is primarily responsible for detecting edges and textures?"},{"answer":"To reduce spatial dimensions and parameters","number":9,"options":["To extract high-level features","To reduce spatial dimensions and parameters","To increase image resolution","To generate class probabilities"],"question":"What is the role of pooling layers in CNNs?"},{"answer":"Encoding","number":10,"options":["Normalization","Resizing","Encoding","Augmentation"],"question":"Which of the following is NOT typically a step in image preprocessing?"}],"title":"Image Data in Machine Vision"}