Results 1  10
of
871,612
A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... Abstract—A sequential algorithm is presented for computing the exact Euclidean distance transform (DT) of a kdimensional binary image in time linear in the total number of voxelsN. The algorithm, which is based on dimensionality reduction and partial Voronoi diagram construction, can be used for co ..."
Abstract

Cited by 99 (3 self)
 Add to MetaCart
Abstract—A sequential algorithm is presented for computing the exact Euclidean distance transform (DT) of a kdimensional binary image in time linear in the total number of voxelsN. The algorithm, which is based on dimensionality reduction and partial Voronoi diagram construction, can be used
Polynomial time approximation schemes for Euclidean Traveling Salesman and other geometric problems
, 1998
"... We present a polynomial time approximation scheme for Euclidean TSP in fixed dimensions. For every fixed c � 1 and given any n nodes in � 2, a randomized version of the scheme finds a (1 � 1/c)approximation to the optimum traveling salesman tour in O(n(log n) O(c) ) time. When the nodes are in � ..."
Abstract

Cited by 390 (2 self)
 Add to MetaCart
approximation algorithms for all these problems achieved a constantfactor approximation. We also give efficient approximation schemes for Euclidean MinCost Matching, a problem that can be solved exactly in polynomial time. All our algorithms also work, with almost no modification, when distance is measured
Linear Time Euclidean Distance Transform Algorithms
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... Two linear time (and hence asymptotically optimal) algorithms for computing the Euclidean distance transform of a twodimensional binary image are presented. The algorithms are based on the construction and regular sampling of the Voronoi diagram whose sites consist of the unit (feature) pixels in t ..."
Abstract

Cited by 93 (0 self)
 Add to MetaCart
Two linear time (and hence asymptotically optimal) algorithms for computing the Euclidean distance transform of a twodimensional binary image are presented. The algorithms are based on the construction and regular sampling of the Voronoi diagram whose sites consist of the unit (feature) pixels
THE EUCLIDEAN KDISTANCE TRANSFORMATION IN ARBITRARY DIMENSIONS: A SEPARABLE IMPLEMENTATION
"... The signed kdistance transformation (kDT) computes the k nearest prototypes from each location on a discrete regular grid within a given D dimensional volume. We propose a new kDT algorithm that divides the problem into D 1dimensional problems and compare its accuracy and computational complexit ..."
Abstract
 Add to MetaCart
The signed kdistance transformation (kDT) computes the k nearest prototypes from each location on a discrete regular grid within a given D dimensional volume. We propose a new kDT algorithm that divides the problem into D 1dimensional problems and compare its accuracy and computational
Robust Distributed Network Localization with Noisy Range Measurements
, 2004
"... This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherw ..."
Abstract

Cited by 389 (20 self)
 Add to MetaCart
This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities
An efficient euclidean distance transform
 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 ..."
Abstract

Cited by 17 (0 self)
 Add to MetaCart
an efficient lineartime algorithm for calculating the true Euclidean distancesquared 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
Distance transforms of sampled functions
 Cornell Computing and Information Science
, 2004
"... This paper provides lineartime algorithms for solving a class of minimization problems involving a cost function with both local and spatial terms. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary ..."
Abstract

Cited by 172 (9 self)
 Add to MetaCart
This paper provides lineartime algorithms for solving a class of minimization problems involving a cost function with both local and spatial terms. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced
A Euclidean distance transform in linear time
, 1998
"... A new linear time scanning algorithm for the exact Euclidean distance transform is presented. It is shorter and uses a smaller data structure than the approximating algorithms of Danielsson. The algorithm consists of two phases. The first phase uses two scans per line to compute the distance transfo ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
A new linear time scanning algorithm for the exact Euclidean distance transform is presented. It is shorter and uses a smaller data structure than the approximating algorithms of Danielsson. The algorithm consists of two phases. The first phase uses two scans per line to compute the distance
Understanding FaultTolerant Distributed Systems
 COMMUNICATIONS OF THE ACM
, 1993
"... We propose a small number of basic concepts that can be used to explain the architecture of faulttolerant distributed systems and we discuss a list of architectural issues that we find useful to consider when designing or examining such systems. For each issue we present known solutions and design ..."
Abstract

Cited by 374 (23 self)
 Add to MetaCart
We propose a small number of basic concepts that can be used to explain the architecture of faulttolerant distributed systems and we discuss a list of architectural issues that we find useful to consider when designing or examining such systems. For each issue we present known solutions and design
AntiAliased Euclidean Distance Transform
, 2010
"... We present a modified distance measure for use with distance transforms of antialiased, area sampled grayscale images of arbitrary binary contours. The modified measure can be used in any vectorpropagation Euclidean distance transform. Our test implementation in the traditional SSED8 algorithm sho ..."
Abstract
 Add to MetaCart
We present a modified distance measure for use with distance transforms of antialiased, area sampled grayscale images of arbitrary binary contours. The modified measure can be used in any vectorpropagation Euclidean distance transform. Our test implementation in the traditional SSED8 algorithm
Results 1  10
of
871,612