Grayscale image threshold segmentation is a commonly used image processing method. Its basic idea is to divide a grayscale image into two parts: one part is the pixels in the image whose pixel value is greater than a certain threshold, and the other part is the pixel whose value is less than or equal to the threshold. of pixels. This process can be regarded as the process of separating the foreground and background in the image, which can be used in image segmentation, target detection, character recognition and other fields. The specific implementation steps of grayscale image threshold segmentation are as follows:
- Convert a color image to a grayscale image.
- Select an appropriate threshold, set the pixel value greater than the threshold in the grayscale image to 255, and set the pixel less than or equal to the threshold to 0 to obtain a binary image.
- Perform subsequent processing on the binary image, such as removing noise, filling holes, segmenting connected domains, etc. In practical applications, how to choose an appropriate threshold is a key issue. Common threshold selection methods include manual selection, adaptive threshold, and threshold selection based on statistical methods. In addition, methods such as multi-threshold segmentation and gradient-based segmentation can also be used for image segmentation. It is necessary to select the appropriate method and parameters according to the specific application scenarios and image characteristics.