基于改进模糊C均值聚类与Otsu的图像分割方法

doi:10.16088/j.issn.1001-6600.2019.04.008

Abstract

Abstract: To solve the problem that the segmentation effect of log image with small difference between background and foreground color is unsatisfactory, an image segmentation method based on fuzzy C-means clustering and Otsu is presented.Firstly, standard log database is used as sample and the log sample image with small difference between background and foreground color is segmented into two categories by using the fuzzy C-means clustering algorithm.Secondly,the criterion function is used to obtain the result of foreground segmentation. Finally,the result is used as input of Otsu algorithm to form the final segmentation of log sample image.The experimental results show that the proposed algorithm is better than the single fuzzy C-means clustering algorithm. Otsu and similar algorithms have better segmentation effect and higher segmentation accuracy, the edge information is well preserved and the average segmentation accuracy is increased by 2 percentage points.

Key words: image segmentation, fuzzy C-mean clustering, Otsu maximum variance, criterion function, accuracy of segmentation

CLC Number:

TP391.41

WANG Xun, LI Tinghui, PAN Xiao, TIAN Yu. Image Segmentation Method Based on Improved Fuzzy C-means Clustering and Otsu Maximum Variance[J].Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 68-73.

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Image Segmentation Method Based on Improved Fuzzy C-means Clustering and Otsu Maximum Variance

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