A COMBINATORIAL APPROACH TO FINGERPRINT BINARIZATION AND MINUTIAE EXTRACTION USING EUCLIDEAN DISTANCE TRANSFORM

2007 ◽  
Vol 21 (07) ◽  
pp. 1141-1158 ◽  
Author(s):  
XUEFENG LIANG ◽  
ARIJIT BISHNU ◽  
TETSUO ASANO
Keyword(s):  
Euclidean Distance ◽  
Linear Time ◽  
Time Algorithm ◽  
Distance Transform ◽  
Fingerprint Matching ◽  
Fingerprint Image ◽  
Binary Images ◽  
Minutiae Extraction ◽  
Euclidean Distance Transform ◽  
Matching Techniques

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.

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.

Linear time Euclidean distance transform algorithms

1995 ◽  
Vol 17 (5) ◽  
pp. 529-533 ◽  
Author(s):  
H. Breu ◽  
J. Gil ◽  
D. Kirkpatrick ◽  
M. Werman
Keyword(s):  
Euclidean Distance ◽  
Linear Time ◽  
Distance Transform ◽  
Euclidean Distance Transform

EFFICIENT ALGORITHMS FOR THE EUCLIDEAN DISTANCE TRANSFORM

1995 ◽  
Vol 05 (02) ◽  
pp. 205-212 ◽  
Author(s):  
SANDY PAVEL ◽  
SELIM G. AKL
Keyword(s):  
Parallel Algorithm ◽  
Euclidean Distance ◽  
Sequential Algorithm ◽  
Distance Transform ◽  
Binary Images ◽  
Euclidean Distance Transform ◽  
Erew Pram ◽  
Time Optimal ◽  
Time Bounds ◽  
And Robotics

The Euclidean Distance Transform is an important computational tool for the processing of binary images, with applications in many areas such as computer vision, pattern recognition and robotics. We investigate the properties of this transform and describe an O(n2) time optimal sequential algorithm. A deterministic EREW-PRAM parallel algorithm which runs in O( log n) time using O(n2) processors and O(n2) space is also derived. Further, a cost optimal randomized parallel algorithm which runs within the same time bounds with high probability, is given.

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