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

2003 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Marcelo Siqueira ◽  
James Gee

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.

Author(s):  
XUEFENG LIANG ◽  
ARIJIT BISHNU ◽  
TETSUO ASANO

Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.


1995 ◽  
Vol 17 (5) ◽  
pp. 529-533 ◽  
Author(s):  
H. Breu ◽  
J. Gil ◽  
D. Kirkpatrick ◽  
M. Werman

2006 ◽  
Author(s):  
Robert Staubs ◽  
Andriy Fedorov ◽  
Leonidas Linardakis ◽  
Benjamin Dunton ◽  
Nikos Chrisochoides

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.


Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf

Author(s):  
Kuryati Kipli ◽  
Mohammed Enamul Hoque ◽  
Lik Thai Lim ◽  
Tengku Mohd Afendi Zulcaffle ◽  
Siti Kudnie Sahari ◽  
...  

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