Thresholding is a technique used in image processing to convert an image into a binary image, where each pixel is either black or white depending on a threshold value. It is used to segment objects in an image by separating the foreground from the background.
Thresholding is a technique used in image processing to separate objects from their background. It is a process of converting a grayscale image into a binary image, where the two colors are black and white. The process involves setting a threshold value, which is a gray level between the two colors. Any pixel with a value above the threshold is set to white, and any pixel with a value below the threshold is set to black.
Thresholding is used in many applications, such as object recognition, image segmentation, and feature extraction. It is also used in medical imaging to detect tumors and other abnormalities. In addition, it is used in document analysis to separate text from background noise.
The most common type of thresholding is called global thresholding. In this method, a single threshold value is used for the entire image. This value is determined by analyzing the histogram of the image, which is a graph of the number of pixels at each gray level. The threshold value is chosen so that it is the point at which the histogram has the greatest change in slope.
Another type of thresholding is called adaptive thresholding. In this method, the threshold value is determined for each pixel individually. This is done by analyzing the local neighborhood of the pixel and calculating a threshold value that is appropriate for that particular pixel.
Thresholding is a powerful tool for image processing, and it can be used to improve the accuracy of object recognition and image segmentation. It is also useful for feature extraction and document analysis. However, it is important to choose the appropriate threshold value for the image, as incorrect values can lead to poor results.