Labeling images using transformations
In this section, let us see the different types of transformations that can be applied to images to generate synthetic data when there is a limited amount of data. In machine learning, shearing and flipping are often used as image augmentation techniques to increase the diversity of training data. It helps improve a model’s ability to recognize objects from different angles or orientations.
Shearing can be used in computer vision tasks to correct for perspective distortion in images. For example, it can be applied to rectify skewed text in scanned documents.
Image shearing is a transformation that distorts an image by moving its pixels in a specific direction. It involves shifting the pixels of an image along one of its axes while keeping the other axis unchanged. There are two primary types of shearing:
- Horizontal shearing: In this case, pixels are shifted horizontally, usually in a diagonal manner, causing an image to slant...
