Description: Binarization is the process of converting an image into a binary form, where each pixel is represented as a value of 0 or 1. This process is fundamental in image processing and computer vision, as it simplifies visual information by reducing it to two colors: black and white. Binarization is used to facilitate the identification of features and patterns in images, which is crucial for tasks such as object segmentation, edge detection, and pattern recognition. There are different methods of binarization, such as global thresholding, where a fixed threshold value is set for the entire image, and adaptive thresholding, which adjusts the threshold based on local characteristics of the image. The choice of binarization method can significantly influence the quality of results in various applications including machine learning and neural networks, where precision in data representation is essential for model performance. In the context of neural networks, binarization can be used to reduce computational complexity and improve efficiency in inference, especially on resource-constrained devices, such as those used in edge AI.
