Description: Thresholding is a fundamental technique in image processing that involves converting a grayscale image into a binary image. This process is achieved by establishing a threshold value that determines which pixels will turn white and which will turn black. Pixels with intensity values above the threshold are assigned one color (usually white), while those below are assigned the other color (usually black). There are several methods to apply thresholding, which can be classified into global and local thresholding. Global thresholding uses a single threshold value for the entire image, while local thresholding adapts the threshold to different regions of the image, allowing for more precise segmentation in areas with varying lighting. This technique is particularly useful for highlighting specific features of an image, facilitating further analysis and processing. Thresholding is used in various applications, from edge detection to object segmentation, and is a crucial step in many computer vision algorithms. Its simplicity and effectiveness make it an essential tool for researchers and professionals in the field of image processing.
Uses: Thresholding is used in a wide variety of applications in image processing, including object segmentation, edge detection, and image enhancement. In medicine, for example, it is applied to analyze medical imaging data such as MRI and X-ray images, allowing doctors to identify structures and anomalies. In industry, it is used for quality control in manufacturing, where defect detection in products is required. It is also common in computer vision, where it is employed for object identification and image classification.
Examples: A practical example of thresholding is its use in segmenting cell images in microscopy, where a threshold can be applied to distinguish between healthy and cancerous cells. Another example is text detection in images, where thresholding helps separate text from the background, facilitating optical character recognition. Additionally, in security systems, it is used to detect motion by segmenting areas of interest in images from surveillance cameras.
