Pixel number clustering-based fuzzy C-average value gray level image splitting method

Abstract

The invention discloses a pixel number clustering-based fuzzy C-average value gray level image splitting method, and mainly solves the problem of low accuracy of splitting of the gray level image. The method is realized by the steps of (1) reading a gray level image and counting a gray level histogram; (2) randomly initializing a clustering center; (3) calculating the Euclidean distance between each gray level and each clustering center; (4) calculating the total number of the pixels contained between each gray level and each clustering center by the Euclidean distance; (5) judging the type of each gray level by the total number of the pixels to obtain a classified result; (6) calculating each type of gray level average value by the classifying result to be used as a new clustering center; (7) calculating a membership matrix according to the clustering center; (8) updating the clustering center by the membership matrix; (9) repeating the steps (3)-(8) until the terminal condition is met, and outputting an updated clustering center; and (10) classifying the gray images by the updated clustering center to obtain a splitting result image. The pixel number clustering-based fuzzy C-average value gray level image splitting method has the advantage of high image splitting precision and can be used for extracting the detail information of the gray level image.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image segmentation method, in particular to a grayscale image segmentation method, which can be used to extract the detailed information of the grayscale image, and provide a better method for subsequent target recognition, feature extraction and other work of image processing. information. Background technique [0002] With the development of various imaging technologies, people's demand and application for image processing are increasing day by day. Image segmentation is one of the very important links in the process of image understanding and the basis of image processing and analysis. Therefore, the research on image segmentation methods is of great significance. Image segmentation is the process of extracting the target or region of interest from the input image according to certain features or feature sets of the image, such as similarity criteria such as texture, grayscal...

AI Technical Summary

Problems solved by technology

However, the shortcomings of the FCM algorithm in processing image segmentation are: (1) When the FCM algorithm is used for image segmentation, it uses random initialization for clustering, which can easily cause misjudgment when the data set contains unequal classes. ; (2) The FCM algorithm uses point-by-point distribution information to display the classification results, which reduces the correlation between pixels of the same gray level in the image, and may cause misclassification for gray levels with a small number of pixels , resulting in over-segmentation of the image and the phenomenon of good or bad results, and sometimes even the failure of image segmentation, causing errors in the information provided for subsequent image processing

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