结合模糊熵和遗传算法的双阈值图像分割

doi:10.3969/j.issn.0255-8297.2014.04.014

Abstract

Abstract: A dual thresholding method is proposed to extract a calibration pattern and a laser spot from a target image simultaneously in a gun barrel camber measurement system. Based on the fuzzy mathematics theoryand a maximum fuzzy entropy criterion, the proposed method can classify the target image into three fuzzysubsets, namely, dark, gray and bright fuzzy subset by their gray levels. Gray levels of the calibration pattern used for distortion correction belong to the dark fuzzy subset, and the ones of the laser spot for measurement belong to the bright fuzzy subset. An improved fuzzy exponential entropy is used as the classification criterion,which can increase classification accuracy. A genetic algorithm is implemented to search for an optimal combination of fuzzy entropy parameters, which has a low computational complexity. There are only four fuzzy entropy parameters in the proposed method, and the search space is small. The proposed method is tested and compared with an Otsu’s dual thresholding method, a dual thresholding method using fuzzy entropy and simulated annealing algorithm, and a dual thresholding method using unimproved fuzzy exponential entropy and genetic algorithm. Experimental results show that the proposed method can determine dual thresholds automatically and efficiently, and has the best segmentation among the tested methods.

Key words: image segmentation, threshold, membership function, fuzzy entropy, genetic algorithm

CLC Number:

ZHENG Yi1,2, ZHENG Ping3,4. Dual Thresholding Method Using Fuzzy Entropy and Genetic Algorithm[J]. Journal of Applied Sciences, 2014, 32(4): 427-433.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2014.04.014

https://www.jas.shu.edu.cn/EN/Y2014/V32/I4/427

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Dual Thresholding Method Using Fuzzy Entropy and Genetic Algorithm

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