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Euclidean Distance Mapping

by Per-Erik Danielsson , 1980
"... Based on a two-component descriptor, a distance label for each point, it is shown that Euclidean distance maps can be generated by effective sequential algorithms. The map indicates, for each pixel in the objects (or the background) of the originally binary picture, the shortest distance to the near ..."
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Based on a two-component descriptor, a distance label for each point, it is shown that Euclidean distance maps can be generated by effective sequential algorithms. The map indicates, for each pixel in the objects (or the background) of the originally binary picture, the shortest distance

Euclidean distance.

by unknown authors
"... assumed to be between 0 and 1. The initial values of all the weight vectors are given between 0 and 1 at random. (SOM1) Input an input vector xj to all the neurons simul-taneously in parallel. (SOM2) Find a winner c by calculating a distance between the input vector xj and the weight vector wi of ea ..."
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assumed to be between 0 and 1. The initial values of all the weight vectors are given between 0 and 1 at random. (SOM1) Input an input vector xj to all the neurons simul-taneously in parallel. (SOM2) Find a winner c by calculating a distance between the input vector xj and the weight vector wi

On the Euclidean Distance of Images

by Liwei Wang, Yan Zhang, Jufu Feng - IEEE Trans. Pattern Anal. Mach. Intell
"... Abstract We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore it is robust to small perturbation of images. We argue that IMED is the only intui ..."
Abstract - Cited by 33 (1 self) - Add to MetaCart
Abstract We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore it is robust to small perturbation of images. We argue that IMED is the only

An efficient euclidean distance transform

by Donald G Bailey - In Combinatorial Image Analysis, IWCIA 2004 , 2004
"... Abstract. Within image analysis the distance transform has many applications. The distance transform measures the distance of each object point from the nearest boundary. For ease of computation, a commonly used approximate algorithm is the chamfer distance transform. This paper presents an efficien ..."
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an efficient linear-time algorithm for calculating the true Euclidean distance-squared of each point from the nearest boundary. It works by performing a 1D distance transform on each row of the image, and then combines the results in each column. It is shown that the Euclidean distance squared transform

EUCLIDEAN DISTANCE GEOMETRY AND APPLICATIONS

by Leo Liberti, Carlile Lavor, Nelson Maculan, Antonio Mucherino
"... Abstract. Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the inputdataconsistsofanincompleteset of distances, and the output is a set of points in Euclidean space that realizes the given distances. We surv ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
Abstract. Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the inputdataconsistsofanincompleteset of distances, and the output is a set of points in Euclidean space that realizes the given distances. We

Euclidean Distance Matrices and Applications

by Nathan Krislock, Henry Wolkowicz
"... Over the past decade, Euclidean distance matrices, or EDMs, have been receiving increased attention for two main reasons. The first reason is that the many applications of EDMs, such as molecular conformation in bioinformatics, dimensionality reduction in machine learning and statistics, and especia ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
Over the past decade, Euclidean distance matrices, or EDMs, have been receiving increased attention for two main reasons. The first reason is that the many applications of EDMs, such as molecular conformation in bioinformatics, dimensionality reduction in machine learning and statistics

Convergent Bounds on the Euclidean Distance

by Yoonho Hwang, Hee-kap Ahn
"... Given a set V of n vectors in d-dimensional space, we provide an efficient method for computing quality upper and lower bounds of the Euclidean distances between a pair of vectors in V. For this purpose, we define a distance measure, called the MS-distance, by using the mean and the standard deviati ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Given a set V of n vectors in d-dimensional space, we provide an efficient method for computing quality upper and lower bounds of the Euclidean distances between a pair of vectors in V. For this purpose, we define a distance measure, called the MS-distance, by using the mean and the standard

On the eigenvalues of Euclidean distance matrices

by A. Y. Alfakih , 2008
"... In this paper, the notion of equitable partitions (EP) is used to study the eigenvalues of Euclidean distance matrices (EDMs). In particular, EP is used to obtain the characteristic polynomials of regular EDMs and non-spherical centrally symmetric EDMs. The paper also presents methods for constructi ..."
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In this paper, the notion of equitable partitions (EP) is used to study the eigenvalues of Euclidean distance matrices (EDMs). In particular, EP is used to obtain the characteristic polynomials of regular EDMs and non-spherical centrally symmetric EDMs. The paper also presents methods

ON EUCLIDEAN DISTANCE MATRICES OF GRAPHS ∗

by Ga Sper, Jakli Č, Jolanda Modic
"... Abstract. In this paper, a relation between graph distance matrices and Euclidean distance matrices (EDM) is considered. It is proven that distance matrices of paths and cycles are EDMs. The proofs are constructive and the generating points of studied EDMs are given in a closed form. A generalizatio ..."
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Abstract. In this paper, a relation between graph distance matrices and Euclidean distance matrices (EDM) is considered. It is proven that distance matrices of paths and cycles are EDMs. The proofs are constructive and the generating points of studied EDMs are given in a closed form. A

PENALIZED EUCLIDEAN DISTANCE REGRESSION

by D. Vasiliu, T. Dey, I. L. Dryden
"... ABSTRACT. A new method is proposed for variable screening, variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The method involves minimizing a penalized Euclidean distance, where the penalty is the geomet ..."
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ABSTRACT. A new method is proposed for variable screening, variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The method involves minimizing a penalized Euclidean distance, where the penalty
Results 1 - 10 of 223,971