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Image Segmentation based on Two -dimension Fuzzy Tsallis-Entropy

机译:基于二维模糊Tsallis熵的图像分割

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摘要

Image processing bears some fuzziness in nature, as a effective mathematical tool for handling the ambiguity, Fuzzy set theory is introduced in the paper to define a new kind of fuzzy entropy, namely Two-dimension Fuzzy Tsallis Entropy(TFTE) and applied in image segmentation following the maximum entropy principle. To overcome the huge calculational burden when generalizing one-dimension entropy to twodimension, the particle swarm optimization (PSO) algorithm was employed to accelerate the search of the optimal threshold. The validity and effectiveness of the presented method is illustrated by experiments and the application of Tsallis Entropy is generalized to fuzzy fields.
机译:图像处理本质上具有一定的模糊性,作为一种有效的模糊处理数学工具,本文引入模糊集理论,定义了一种新型的模糊熵,即二维模糊Tsallis熵(TFTE),并应用于图像分割中。遵循最大熵原理。为了克服将一维熵推广为二维时的巨大计算负担,采用了粒子群算法(PSO)来加速最优阈值的搜索。通过实验说明了该方法的有效性和有效性,并将Tsallis熵的应用推广到了模糊领域。

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