In this paper, we study the relations (L, )-fuzzy topologies and (L, )-filters on strictly two-sided, commutative quantale lattices (L, ) and (L, ∗). Mathematics Subject Classification: 03E72, 54A40, 54B10

In this paper, we study (L, )-fuzzy topologies induced by operations and (L, )-filters on strictly two-sided, commutative quantale lattices (L, ) and (L, ∗). Mathematics Subject Classification: 03E72, 54A40, 54B10

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 p...

Multi-fuzzy set theory is an extension of fuzzy set theory, L-fuzzy set theory and Atanassov intuitionistic fuzzy set theory. In this paper we study the relation between Atanassov intuitionistic fuzzy and the proposed extension called Multi-fuzzy. Also we present the notion of Multi-fuzzy mappings and Atanassov Intuitionistic Fuzzy Sets Generating Maps. Mathematics Subject Classification: MSC: ...

In this paper, we study countably compact fuzzy sets using the definition of C. K. Wong [10] and obtain its several properties. 1. Introduction The concept of fuzzy set and fuzzy set operations were first introduced by L. A. Zadeh [11] in1965. Several other mathematicians studied fuzzy sets in various areas in mathematics. Firstly, C. L. Chang [2] in 1968 developed the theory of fuzzy topologic...

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a pre-filtering technique for Gaussian and impulse noise elimination, and a s...

In this paper, we propose a weighted principal component analysis (WPCA) using the result of fuzzy clustering [4]. The principal component analysis (PCA) [1], [7] is one widely used and well-known data analysis method. However there is a problem, when the data does not have a structure that the PCA can capture we cannot obtain any satisfactory results. For the most part, this is due to the unif...

Introduction and motivation To explain the motivation of our work we give a short glimpse into the history of Fuzzy Topology called more recently Lattice-Valued Topology or Many Valued Topology. In 1968, C. L. Chang [1] introduced the notion of a fuzzy topology on a set X as a subset τ ⊆ [0, 1] satisfying the natural counterparts of the axioms of topology: (1) 0X , 1X ∈ τ ; (2) U, V ∈ τ ⇒ U ∧ V...

In this paper the concept of metacompactness in L-topologicalspaces is introduced by means of point finite families of L-fuzzy sets. Thisfuzzy metacompactness is a natural generalization of Lowen fuzzy compactness.Further a characterization of fuzzy metacompactness in the weakly inducedL-topological spaces is also obtained.

A definition of L-fuzzy bags is introduced and studied. In this approach, according to the concept given by M. Delgado et al. (2009), each bag has two parts: function and summary information. Then, the definition of L-fuzzy bag expected value is introduced. In the case L = [0, 1], several integral-based fuzzy bag expected values are prepared. By some examples, the new concepts are illustrated.