Description: Binarization techniques are methods used in computer vision to convert grayscale images into binary images, where each pixel is classified as either black or white. This process is fundamental for simplifying visual information and facilitating image analysis. Binarization relies on applying a threshold that determines the intensity value separating pixels into two categories. There are two main approaches: global thresholding, which uses a single threshold value for the entire image, and adaptive thresholding, which adjusts the threshold based on local characteristics of the image. The choice of the appropriate binarization technique depends on lighting conditions, contrast, and the nature of the image content. Binarization is crucial in tasks such as object segmentation, character recognition, and edge detection, as it highlights important features while removing irrelevant details. In summary, binarization techniques are essential tools in image processing that transform complex visual data into more manageable and useful representations for various applications in computer vision.
