基于模糊Renyi熵和混沌差分进化算法的红外电图像分割

S. Fan, Shu-hong Yang
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引用次数: 13

摘要

红外热像仪在电力设备监测中具有重要意义,但红外图像本身具有模糊性,因此红外电图像的分割是一项具有挑战性的任务。为了处理图像的模糊性,利用模糊隶属度将图像的直方图转换为模糊域,并根据模糊人义熵(FRE)的定义分别计算目标和背景的模糊熵。然后,以隶属函数参数组合为单个向量,根据最大熵原理,提出了基于Logistic映射的混沌微分进化(CDE)算法寻找最优阈值;通过与其他典型方法的比较,验证了该方法的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Infrared Electric Image Segmentation Using Fuzzy Renyi Entropy and Chaos Differential Evolution Algorithm
Infrared thermograph is of great significance in electric equipment monitoring, but infrared images are by nature fuzzy and thus the segmentation of infrared electric image is a challenging task. To handing this ambiguity, the histogram of image is transformed into fuzzy domain employing fuzzy membership, and the fuzzy entropy of object and background is computed respectively according to the definition of Fuzzy Renyi Entropy(FRE). Then, with combinations of the membership function's parameters as individual vectors, a chaos differential evolution (CDE) algorithm based on Logistic map was presented to find the optimum threshold following maximum entropy principle. Compared with other typical methods, the presented method is verified to be more effective and less time-consuming.
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