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Interval-valued and intuitionistic fuzzy mathematical morphologies as special cases of L-fuzzy mathematical morphology

机译:区间值直觉模糊数学形态作为L-模糊数学形态的特例

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Mathematical morphology (MM) offers a wide range of tools for image processing and computer vision. MM was originally conceived for the processing of binary images and later extended to gray-scale morphology. Extensions of classical binary morphology to gray-scale morphology include approaches based on fuzzy set theory that give rise to fuzzy mathematical morphology (FMM). From a mathematical point of view, FMM relies on the fact that the class of all fuzzy sets over a certain universe forms a complete lattice. Recall that complete lattices provide for the most general framework in which MM can be conducted. The concept of L-fuzzy set generalizes not only the concept of fuzzy set but also the concepts of interval-valued fuzzy set and Atanassov's intuitionistic fuzzy set. In addition, the class of L-fuzzy sets forms a complete lattice whenever the underlying set L constitutes a complete lattice. Based on these observations, we develop a general approach towards L-fuzzy mathematical morphology in this paper. Our focus is in particular on the construction of connectives for interval-valued and intuitionistic fuzzy mathematical morphologies that arise as special, isomorphic cases of L-fuzzy MM. As an application of these ideas, we generate a combination of some well-known medical image reconstruction techniques in terms of interval-valued fuzzy image processing.
机译:数学形态学(MM)为图像处理和计算机视觉提供了广泛的工具。 MM最初是为处理二进制图像而设计的,后来扩展到灰度形态。从经典二进制形态学到灰度形态学的扩展包括基于模糊集理论的方法,这些方法产生了模糊数学形态学(FMM)。从数学的角度来看,FMM依赖于这样的事实,即某个宇宙上所有模糊集的类形成一个完整的晶格。回想一下,完整的格子提供了可以进行MM的最通用框架。 L-模糊集的概念不仅概括了模糊集的概念,而且概括了区间值模糊集和Atanassov直觉模糊集的概念。此外,每当底层集合L构成一个完整的点阵时,L-模糊集的类就形成一个完整的点阵。基于这些观察,我们在本文中开发了一种针对L-模糊数学形态学的通用方法。我们的重点尤其是针对区间值和直觉模糊数学形态学的连接词的构造,这些词形是L-模糊MM的特殊同构情况产生的。作为这些思想的应用,我们就区间值模糊图像处理而言,结合了一些著名的医学图像重建技术。

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