Exploring Image Processing and ML Concepts

What is the purpose of image resizing in preprocessing?

• To reduce file size for faster loading times.

• To apply filters for artistic effects.

• To enhance image color saturation for better visibility.

• To standardize image dimensions for uniformity and improved model performance.

How does cropping an image affect its dimensions?

• Cropping an image decreases its dimensions by removing parts of the image.

• Cropping an image only changes its color, not its size.

• Cropping an image increases its dimensions by adding more space.

• Cropping an image has no effect on its dimensions at all.

What function is used to read an image in OpenCV?

• cv2.imread()

• cv2.getImage()

• cv2.loadImage()

• cv2.readImage()

Which function displays an image in a window using OpenCV?

• cv2.showImage()

• cv2.displayImage()

• cv2.render()

• cv2.imshow()

What does HSV stand for in color space representation?

• Hue, Saturation, Value

• Hue, Saturation, Brightness

• High, Standard, Value

• Hue, Saturation, Lightness

How do you convert an image to HSV color space using OpenCV?

• Use cv2.convertColor(image, cv2.COLOR_RGB2HSV)

• Transform image using cv2.toHSV(image)

• Use cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

• Apply cv2.changeColor(image, cv2.COLOR_BGR2HSV)

What is the purpose of the cv2.inRange function?

• To resize an image to a specified dimension.

• To create a binary mask based on a specified range of pixel values.

• To enhance the color saturation of an image.

• To apply a Gaussian blur effect on an image.

What is supervised learning?

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a type of reinforcement learning.

• Supervised learning is a method that requires no data for training.

• Supervised learning is a machine learning approach that uses labeled data to train models.

What is the main difference between supervised and unsupervised learning?

• Supervised learning requires more computational power than unsupervised learning.

• Supervised learning can only be applied to classification tasks.

• Unsupervised learning is always more accurate than supervised learning.

• The main difference is that supervised learning uses labeled data while unsupervised learning uses unlabeled data.

What is overfitting in machine learning?

• Overfitting happens when a model is trained on too much data, leading to confusion.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.