Thresholding

Using a preset threshold value, the process of thresholding divides a picture into two classes: background and foreground. It is a straightforward yet efficient method of image segmentation. One class is assigned to pixels whose intensity values are above the threshold, and another class is assigned to pixels whose values are below it.
 

The Process

Analysis of the Intensity Histogram: Determine possible threshold values by examining the intensity histogram of the image.
Threshold Selection: Utilizing the histogram and/or other criteria, determine an appropriate threshold value.
Pixel classification: It involves comparing the intensity of each pixel to the threshold.
Creation of Binary Images: Assign the values of the pixels above and below the threshold, respectively (for example, 255 for white and 0 for black).

Types of Thresholding

• Global Thresholding: The entire image is subject to a single threshold value.
• Otsu's Method: Maximizes the inter-class variation to automatically identify the ideal threshold value.
• Local Thresholding: Various parts of the image are subjected to distinct threshold values.
• Adaptive Thresholding: The threshold is determined by taking into account the local image properties (such as the mean and standard deviation).
• Multilevel Thresholding: An picture can be divided into several parts by applying different thresholds.
   

Applications

Thresholding is widely used in various image processing applications:

Object Detection: Identifying objects of interest in an image.
Image Segmentation: Dividing an image into meaningful regions.
Feature Extraction: Extracting relevant features from images.
Image Compression: Reducing image size by representing regions with a single value.