Search results for "neighborhood system"
showing 3 items of 3 documents
Pointwise k-Pseudo Metric Space
2021
In this paper, the concept of a k-(quasi) pseudo metric is generalized to the L-fuzzy case, called a pointwise k-(quasi) pseudo metric, which is considered to be a map d:J(LX)×J(LX)⟶[0,∞) satisfying some conditions. What is more, it is proved that the category of pointwise k-pseudo metric spaces is isomorphic to the category of symmetric pointwise k-remote neighborhood ball spaces. Besides, some L-topological structures induced by a pointwise k-quasi-pseudo metric are obtained, including an L-quasi neighborhood system, an L-topology, an L-closure operator, an L-interior operator, and a pointwise quasi-uniformity.
A fuzzification of the category of M-valued L-topological spaces
2004
[EN] A fuzzy category is a certain superstructure over an ordinary category in which ”potential” objects and ”potential” morphisms could be such to a certain degree. The aim of this paper is to introduce a fuzzy category FTOP(L,M) extending the category TOP(L,M) of M-valued L- topological spaces which in its turn is an extension of the category TOP(L) of L-fuzzy topological spaces in Kubiak-Sostak’s sense. Basic properties of the fuzzy category FTOP(L,M) and its objects are studied.
Spatiocolorimetric neighborhood hypergraph and Image Processing Applications : Noise Removal and Edge Detection.
2004
In this document, we are interested in image modeling by the means of the hypergraph theory. Our contribution is essentially centered on the determination of the properties resulting from this theory and on the analysis from their adequacy with image problems, particularly edge and noise detection.First, we study the image spatiocolorimetric neighborhood hypergraph representation. Three representations are respectively presented incorporating global properties, local properties and similarity functions. Then, we use the hypergraph properties generated by the representation in order to define the structural models of noise and edge. This enables us to deduce the algorithms of noise suppressi…