Because classical fast vector quantization(VQ) algorithms can't be used in the LSF vector quantizers that use varying weighted Euclidean distance, a novel fast VQ search algorithm —CRVQCS (Constrained Range Vector Quantization based on Component Searching) is presented in this paper. The CRVQ-CS algorithm works well with the varying weighted Euclidean distance and yields the same result as full...

In this paper, we present two feature extraction methods for two-dimensional face recognition. Our approaches are based on facial feature points detection then compute the Euclidean Distance between all pairs of this points for a first method (ED-FFP) and Geodesic Distance in the second approach (GD-FFP). These measures are employed as inputs to a commonly used classification techniques such as...

This paper is an attempt of proposing the processing approach of neutrosophic technique in image processing. As neutrosophic sets is a suitable tool to cope with imperfectly defined images, the properties, basic operations distance measure, entropy measures, of the neutrosophic sets method are presented here. İn this paper we, introduce the distances between neutrosophic sets: the Hamming dista...

Recently, many scholars investigated interval, triangular, and trapezoidal approximations of fuzzy numbers. These researches can be grouped into two classes: the Euclidean distance class and the non-Euclidean distance class. Most approximations in the Euclidean distance class can be calculated by formulas, but calculating approximations in the other class is more complicated. In this paper, we ...

The ability to estimate distance and time to spatial goals is fundamental for survival. In cases where a region of space must be navigated around to reach a location (circumnavigation), the distance along the path is greater than the straight-line Euclidean distance. To explore how such circumnavigation impacts on estimates of distance and time, we tested participants on their ability to estima...

Let x1, · · · ,xn be points randomly chosen from a set G ⊂ R and f(x) be a function. The Euclidean random matrix is given by Mn = (f(∥xi − xj∥))n×n where ∥ · ∥ is the Euclidean distance. When N is fixed and n → ∞ we prove that μ̂(Mn), the empirical distribution of the eigenvalues of Mn, converges to δ0 for a big class of functions of f(x). Assuming both N and n go to infinity proportionally, we ...

The Euclidean distance matrix (EDM) completion problem and the positive semidefinite (PSD) matrix completion problem are considered in this paper. Approaches to determine the location of a point in a linear manifold are studied, which are based on a referential coordinate set and a distance vector whose components indicate the distances from the point to other points in the set. For a given ref...

Let x1, · · · ,xn be points randomly chosen from a set G ⊂ R and f(x) be a function. A special Euclidean random matrix is given by Mn = (f(∥xi − xj∥))n×n. When p is fixed and n → ∞ we prove that μ̂(Mn), the empirical distribution of the eigenvalues of Mn, converges to δ0 for a big class of functions of f(x). Assuming both p and n go to infinity with n/p → y ∈ (0,∞), we obtain the explicit limit ...