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Image thresholding is a technique used in image processing to segment objects from the background. It simplifies images by converting them into binary images, making analysis easier for various applications.
Theory of Image Thresholding
Thresholding involves selecting a specific intensity value, called the threshold, to classify pixels. Pixels with intensity values above the threshold are set to one value (e.g., white), while those below are set to another (e.g., black). This process helps distinguish objects from the background.
Implementation Methods
There are several methods to determine the optimal threshold:
- Global Thresholding: Uses a single threshold value for the entire image.
- Otsu’s Method: Automatically calculates the threshold by minimizing intra-class variance.
- Adaptive Thresholding: Calculates thresholds for smaller regions, useful for images with varying illumination.
Applications of Image Thresholding
Thresholding is widely used in various fields, including:
- Medical imaging for tumor detection
- Object recognition in computer vision
- Document image analysis for text extraction
- Quality control in manufacturing