Description: Global thresholding is an image segmentation method used in computer vision to convert a grayscale image into a binary image. This process relies on applying a single threshold value to the entire image, allowing for the distinction between pixels that belong to an object of interest and those that are part of the background. The technique is based on the premise that pixels with intensity values above the threshold are classified as part of the object, while those below are considered background. This approach is particularly useful in situations where lighting is uniform and the contrast between the object and the background is significant. Global thresholding is a simple and efficient method that allows for rapid image segmentation, thus facilitating subsequent analysis. However, its effectiveness can be compromised under variable lighting conditions or in images with noise, where a single threshold cannot adequately capture the complexity of the scene. Despite its limitations, it remains a fundamental technique in image processing, serving as a foundation for more advanced segmentation and image analysis methods.
History: Global thresholding has its roots in the early developments of image processing in the 1960s. One of the most well-known methods was proposed by William K. Pratt in his book ‘Digital Image Processing’ published in 1978, where segmentation techniques, including thresholding, are described. Over the years, the technique has evolved with advancements in technology and the development of more sophisticated algorithms, but the basic concept of applying a single threshold to an image has remained.
Uses: Global thresholding is used in various image processing applications, such as edge detection, object segmentation, and image enhancement. It is common in document processing systems where distinguishing content from the background is required. It is also applied in medicine to segment MRI or CT images, facilitating the identification of anatomical structures.
Examples: A practical example of global thresholding is its use in satellite image segmentation to identify areas of vegetation. By applying an appropriate threshold, green zones can be distinguished from urban or desert areas. Another example is in the security industry, where it is used to detect movement in surveillance camera images by segmenting potential intrusions from the background.
