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Fuzzy C-mean image segmentation algorithm with dynamic parameters and edge subdivision


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Fuzzy C-mean image segmentation algorithm with dynamic parameters and edge subdivision
Wang Zhuo1Zhang Changsheng1Qian Junbing2
1.School of Information Engineering and Automation; 2.Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology,Kunming 650500,China
fringe subdivision dynamic parameters fuzzy C-mean image segmentation local grayscale compression spatial aggregation degree sliding mask
Aiming at the problems of poor noise resistance and low segmentation accuracy for traditional fuzzy C-mean(FCM)algorithm applied to image segmentation,a fuzzy C-mean image segmentation algorithm with dynamic parameters and edge subdivision is proposed here. An image is compressed by local grayscale and the edge pixel information is subdivided to enhance the edge pixel separability. The concept of spatial aggregation degree is proposed to update the membership degree of pixels,and a sliding mask is designed to subdivide pixels into information points,noise points and boundary points. According to the pixel category,dynamic parameters are introduced to adjust the weight of the pixel to enhance the self-adaptability of the algorithm. According to the result of neighborhood pixel clustering,the classification of center pixels is reclassified to improve the fault tolerance of the algorithm. Three pictures are used to test the performance of the algorithm,and the experimental results of the algorithm are compared with those of FCM-S1,FCM-S2 and fuzzy C-means of diamond(FCMD). Experimental results show that the segmentation effect of the proposed algorithm is better than those of the comparison algorithm except for the result of test image Cameraman. Partition coefficient Vpc can be improved by 0.019 3~0.052 9,and partition entropy Vpe can be reduced by 0.026 9~0.094 4.


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Last Update: 2020-06-30