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Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy.
The proposed tool is based on fuzzy Entropy and t norm based fuzzy compromise programming to deal with the vagueness of human judgments.
In this paper, an efficient algorithm is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy.
The proposed definition evaluation based on fuzzy entropy is used to calculate the image definition value.
Compared to the traditional methods, the proposed image definition measure based on fuzzy entropy has two important advantages.
First, a new method is proposed to model the complex imaging process and evaluate the image definitions based on fuzzy entropy.
Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result.
The novel hybrid learning algorithm is based on the fuzzy entropy clustering (FEC), the modified particle swarm optimization (MPSO), and the recursive singular value decomposition (RSVD).
To address these problems, we propose a method to define the image definition evaluation function based on the fuzzy entropy, which was proposed to measure the fuzzy degree of fuzzy set reasonably [17].
First, we propose a new image definition evaluation method based on the fuzzy entropy, which can reduce the negative effects of noise and variations of light intensity and lens magnification.
To evaluate the image definition, we compute an image contrast map based on the fuzzy entropy defined above firstly, because the image contrast is closely related to the image definition.
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