Multi-threshold-value segmentation method based on gray level histogram

Abstract

The invention discloses a multi-threshold-value segmentation method based on a gray level histogram. According to the multi-threshold-value segmentation method, the maximum between-cluster variance principle is used, a dichotomy serves as the basis, and a minimal-value point in the gray level histogram serves as a reference point of multi-threshold-value segmentation. The multi-threshold-value segmentation method includes the steps of firstly, finding out a first threshold value of the gray level histogram according to the otsu method, secondly, respectively solving to obtain the maximum between-cluster variances of the two parts of the segmented histogram based on the dichotomy, thirdly, comparing the two variances, and finding out a threshold value corresponding the maximum variance to serve as a threshold-value point of next-time segmentation, fourthly, executing the three steps repeatedly until the given number of the threshold values is achieved, fifthly, finding out all wave trough points according to the smoothed histogram, and finally, comparing all the obtained threshold values with the wave trough points, and finding out the wave trough point closest to the threshold values to serve as the final threshold value. Compared with a traditional multi-threshold-value segmentation method, the multi-threshold-value segmentation method has the advantages that a part of segmentation noise is eliminated, and the segmentation effect and the adaptability are better than existing algorithms.

Description

technical field [0001] The invention belongs to the threshold segmentation technology of grayscale images, in particular to a multi-threshold segmentation method based on the histogram of grayscale images. Background technique [0002] Image segmentation is an important processing technology in image processing, which has a wide range of applications. The purpose of image segmentation is to obtain the area required by the user, which is the key premise for further processing the obtained target. Therefore, researchers have proposed various methods for image segmentation. Among them, threshold-based image segmentation is an effective method for image segmentation. The Otsu method is a classic thresholding method. The Otsu method is also called the maximum inter-class variance method. It was proposed by the Japanese scholar Otsu and 1979. Due to the Otsu threshold method for image segmentation, the algorithm is simple, stable, and can automatically perform threshold segment...

AI Technical Summary

Problems solved by technology

However, when the single threshold is more than 4, the execution time of the algorithm is still very long, and the segmented image often contains noise

Afterwards, the researchers proposed improved multi-threshold algorithms and fast implementations. Most of these algorithms were mainly improved from the complexity of the algorithm, and the segmentation effect of the threshold was seldom considered.

The fast sending algorithm proposed by Li Zhexue et al. has low time complexity, but in many cases there are mis-segmentation

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