The area-to-point kriging method (ATPK) is an important technology of downscaling without auxiliary information in remote sensing. However, it uses a constant semivariogram to downscale geospatial variables, which ignores the spatial heterogeneity between objects. To deal with this kind heterogeneity, study proposes fuzzy object-based ATPK method, mainly consists three steps: extraction objects...

X-ray mapping images of polished sections are classified using two unsupervised clustering algorithms. The methods applied are the k-means algorithm and an extended spectral fuzzy c-means algorithm. The extentions include new types of memberships that are related to the contextual information. In addition to the traditional spectral membership we apply a spatial membership and a parental member...

The solar chromosphere consists of three classes which contribute differentially to ultraviolet radiation reaching the earth. We describe a data set of solar images, means of segmenting the images into the constituent classes, and a novel high-level representation for compact objects based on a triangulated spatial ‘membership function.’ Such representations are fitted in a variable-dimension M...

this paper presents a fuzzy goal programming (fgp) methodology for solving bi-level quadratic programming (blqp) problems. in the fgp model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vec...

Measurement results contain different kinds of uncertainty. Besides systematic errors andrandom errors individual measurement results are also subject to another type of uncertainty,so-called emph{fuzziness}. It turns out that special fuzzy subsets of the set of real numbers $RR$are useful to model fuzziness of measurement results. These fuzzy subsets $x^*$ are called emph{fuzzy numbers}. The m...

Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means cluste...

The aim of this paper is to introduce a new methodology for micro-pattern analysis in digital images. The gray-level pixels’ structure in an image neighborhood describes a spatial specific context. Edge, line, spot, blob, corner or texture can be described by this structure. The gray-level values of the image pixel are interpreted as a fuzzy set, and each pixel gray-level as a fuzzy number. A m...

Uncertain set theory is a generalization of uncertainty theory that has become a new branch of mathematics for modeling human belief degrees. Uncertain set is a fundamental concept to describe unsharp concepts in uncertain set theory. The moments are important characteristics of an uncertain set. This paper studies the moments and central moments of uncertain set and gives some formulas to calc...

We consider pattern matching problems where patterns are presented as sequences of fuzzy constraints on input elements. Given an infinite alphabet A, a pattern P [α,β] is a sequence ­ μi1 , μi2 , ..., μim ® of membership functions μij defined on A. The pattern P fuzzy matches an input sequence t ∈ A∗ if t = ux1x2 · · ·xmv such that