Research on Fast Multi-Threshold Image Segmentation Technique Using Histogram Analysis

This paper investigates a method for the multi-threshold segmentation of grayscale imaging using the local minimum points of a histogram curve as the segmentation threshold. By smoothing the histogram curve and judging the conditions, the expected peaks and valleys are identified, and the corresponding minimum points are used as segmentation thresholds to achieve fast multi-threshold image segmentation. Compared to the OTSU method (maximum between-class variance) for multi-threshold segmentation and the region growing method, this method has less computational complexity. In the recognition and segmentation process of solder pads with adhesion of underfill in LED Chips, the segmentation time is less than one percent of that of the OTSU method and the region growing method. The segmentation effect is better than the OTSU method and the region growing method, and it can achieve fast multi-threshold segmentation of images. Moreover, it has strong adaptability to the differences in the overall grayscale of images, meeting the requirements for high UPH (Units Per Hour) in industrial production lines.