scholarly journals Fast Exact Euclidean Distance (FEED): A New Class of Adaptable Distance Transforms

2014 ◽  
Vol 36 (11) ◽  
pp. 2159-2172 ◽  
Author(s):  
Theo E. Schouten ◽  
Egon L. van den Broek
Keyword(s):  
Euclidean Distance ◽  
New Class ◽  
Distance Transforms

scholarly journals N-D Linear Time Exact Signed Euclidean Distance Transform

2003 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Marcelo Siqueira ◽  
James Gee
Keyword(s):  
Computer Vision ◽  
Euclidean Distance ◽  
Linear Time ◽  
Small Time ◽  
Direct Application ◽  
Distance Transform ◽  
Fast Computation ◽  
Distance Transforms ◽  
Euclidean Distance Transform ◽  
Image Pixels

Fast computation of distance transforms find direct application in various computer vision problems. Currently there exists two image filters in the ITK library which can be used to generate distance maps. Unfortunately, these filters produce only approximations to the Euclidean Distance Transform (EDT). We introduce into the ITK library a third EDT filter which was developed by Maurer {} . In contrast to other algorithms, this algorithm produces the exact signed squared EDT using integer arithmetic. The complexity, which is formally verified, is O(n) O(n) with a small time constant where n n is the number of image pixels.

DATA PARALLEL COMPUTATION OF EUCLIDEAN DISTANCE TRANSFORMS

1992 ◽  
Vol 02 (04) ◽  
pp. 331-339 ◽  
Author(s):  
TERRY BOSSOMAIER ◽  
NATALINA ISIDORO ◽  
ADRIAN LOEFF
Keyword(s):  
High Speed ◽  
Euclidean Distance ◽  
Maximum Error ◽  
Parallel Computers ◽  
Data Sets ◽  
Data Parallel ◽  
Distance Transforms ◽  
Diverse Data ◽  
Mimd Architectures ◽  
New Strategies

The Euclidean Distance Transform is an important, but computationally expensive, technique of computational geometry, with applications in many areas including image processing, graphics and pattern recognition. Since the data sets used are typically large, one might hope that parallel computers would be suitable for its determination. We show that existing parallel algorithms perform poorly on certain data sets and introduce new strategies. These achieve high speed on diverse data sets, but fail occasionally in pathological cases. We determine the maximum error in such cases and demonstrate that it is satisfactorily low. Although adequate efficiency is achievable on SIMD machines, we demonstrate that problems of this kind are data parallel yet best suited to MIMD architectures.

Precise Euclidean distance transforms in 3D from voxel coverage representation

2015 ◽  
Vol 65 ◽  
pp. 184-191 ◽  
Author(s):  
Vladimir Ilić ◽  
Joakim Lindblad ◽  
Nataša Sladoje
Keyword(s):  
Euclidean Distance ◽  
Distance Transforms

scholarly journals Parallel N-Dimensional Exact Signed Euclidean Distance Transform

2006 ◽  
Author(s):  
Robert Staubs ◽  
Andriy Fedorov ◽  
Leonidas Linardakis ◽  
Benjamin Dunton ◽  
Nikos Chrisochoides
Keyword(s):  
Data Structures ◽  
Euclidean Distance ◽  
Parallel Implementation ◽  
Three Dimensional ◽  
Distance Transform ◽  
Distance Transforms ◽  
Euclidean Distance Transform ◽  
Use Of Data ◽  
Computation Speed ◽  
Standard Tests

The computation speed for distance transforms becomes important in a wide variety of image processing applications. Current ITK library filters do not see any benefit from a multithreading environment. We introduce a three-dimensional signed parallel implementation of the exact Euclidean distance transform algorithm developed by Maurer et al. with a theoretical complexity of O(n/p) for n voxels and p threads. Through this parallelization and efficient use of data structures we obtain approximately 3 times mean speedup on standard tests on a 4-processor machine compared with the current ITK exact Euclidean distance transform filter.

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