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Ultrafuzziness Optimization Based on Type II Fuzzy Sets for Image Thresholding

机译:基于II类模糊集的图像模糊阈值超模糊优化

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Image thresholding is one of the processing techniques to provide high quality preprocessed image. Image vagueness and bad illumination are common obstacles yielding in a poor image thresholding output. By assuming image as fuzzy sets, several different fuzzy thresholding techniques have been proposed to remove these obstacles during threshold selection. In this paper, we proposed an algorithm for thresholding image using ultrafuzziness optimization to decrease uncertainty in fuzzy system by common fuzzy sets like type II fuzzy sets. Optimization was conducted by involving ultrafuzziness measurement for background and object fuzzy sets separately. Experimental results demonstrated that the proposed image thresholding method had good performances for images with high vagueness, low level contrast, and grayscale ambiguity.
机译:图像阈值化是提供高质量预处理图像的处理技术之一。图像模糊和照明不良是产生不良图像阈值输出的常见障碍。通过假设图像为模糊集,已经提出了几种不同的模糊阈值处理技术以在阈值选择期间消除这些障碍。在本文中,我们提出了一种使用超模糊优化对图像进行阈值处理的算法,以减少常见的模糊集(如II型模糊集)对模糊系统的不确定性。通过分别对背景模糊集和对象模糊集进行超模糊测量来进行优化。实验结果表明,所提出的图像阈值处理方法对于模糊性高,对比度低,灰度模糊的图像具有良好的性能。

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