Fast segmentation method for grayscale image histogram based on K-harmonic means clustering

Abstract

The invention discloses a fast segmentation method for a grayscale image histogram based on K-harmonic means clustering. The fast segmentation method comprises the steps of inputting a grayscale image, and calculating to acquire a grayscale histogram; determining the clustering segmentation number m of the image, and setting the value of a K-harmonic means power exponent p; calculating a new membership function value matrix U(t+1) according to a KHM (K-harmonic means) clustering principle; calculating an image weighting function vector W; calculating a newly acquired clustering center vector Cj; stopping algorithm iteration if the norm U(t+1)-U(t) is less than epsilon or reaches an allowable maximum iteration value; if not, supposing t=t+1, and carrying out algorithm iteration continuously; and segmenting the image according to a membership matrix U(t) acquired by convergence. The fast segmentation method disclosed by the invention is not sensitive to a clustering initial value, the stability is high, and the segmentation quality of the grayscale image is improved. In addition, the fast segmentation method is high in time efficiency, capable of realizing a real-time segmentation task, and applicable to task occasions with a high real-time requirement.

Description

technical field [0001] The invention relates to the technical field of image segmentation in computer vision, in particular to a method for rapidly segmenting grayscale image histograms based on harmonic K-means clustering. Background technique [0002] In the field of computer vision, image segmentation is the basis for effective analysis and understanding of image content. It has a wide range of applications in industrial practice, modern agricultural production, medical diagnosis, etc., such as surface defect detection of workpieces, image-based fruit Quality inspection, blood cell segmentation in medical microscopy images, etc. Due to the differences in imaging technologies and the complexity of natural images, it is very difficult to achieve accurate image segmentation in specific application scenarios. Therefore, domestic and foreign scholars have conducted extensive research on image segmentation methods for different application requirements. , and proposed various ...

AI Technical Summary

Problems solved by technology

[0004] Because the image data is generally large in scale, for a digital image with a size of M×N, the number of pixels is M×N, and the time efficiency of directly applying the KM or FCM algorithm to segment the image is very low. The application of these algorithms will be greatly limited in the image processing tasks of industrial practice that require high real-time performance.

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