An optimal parallel algorithm for the Euclidean distance maps of binary images

2002 ◽  
Author(s):  
A. Fujiwara ◽  
T. Masuzawa ◽  
H. Fujiwara
Keyword(s):  
Parallel Algorithm ◽  
Euclidean Distance ◽  
Binary Images

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.

An optimal parallel algorithm for the Euclidean distance maps of 2-D binary images

1995 ◽  
Vol 54 (5) ◽  
pp. 295-300 ◽  
Author(s):  
Akihiro Fujiwara ◽  
Toshimitsu Masuzawa ◽  
Hideo Fujiwara
Keyword(s):  
Parallel Algorithm ◽  
Euclidean Distance ◽  
Binary Images

A PARALLEL ALGORITHM FOR ANALYZING CONNECTED COMPONENTS IN BINARY IMAGES

1992 ◽  
Vol 06 (02n03) ◽  
pp. 315-333 ◽  
Author(s):  
FRANCO CHIAVETTA ◽  
VITO DI GESÙ ◽  
ROSALIA RENDA
Keyword(s):  
Parallel Algorithm ◽  
Parallel Implementation ◽  
Discrete Space ◽  
Space Complexity ◽  
Connected Components ◽  
Two Dimensional ◽  
Binary Images ◽  
Cylindrical Algebraic Decomposition ◽  
Time Space ◽  
Connectivity Degree

In this paper, a parallel algorithm for analyzing connected components in binary images is described. It is based on the extension of the Cylindrical Algebraic Decomposition (CAD) to a two-dimensional (2D) discrete space. This extension allows us to find the number of connected components, to determine their connectivity degree, and to solve the visibility problem. The parallel implementation of the algorithm is outlined and its time/space complexity is given.

TWO ALGORITHMS FOR COMPUTING THE EUCLIDEAN DISTANCE TRANSFORM

2001 ◽  
Vol 01 (04) ◽  
pp. 635-645 ◽  
Author(s):  
MARINA L. GAVRILOVA ◽  
MUHAMMAD H. ALSUWAIYEL
Keyword(s):  
Parallel Algorithm ◽  
Euclidean Distance ◽  
Linear Array ◽  
Linear Time ◽  
Sequential Algorithm ◽  
Distance Transform ◽  
Optimal Algorithms ◽  
Euclidean Metric ◽  
Input Size ◽  
Feature Transform

Given an n × n binary image of white and black pixels, we present two optimal algorithms for computing the distance transform and the nearest feature transform using the Euclidean metric. The first algorithm is a fast sequential algorithm that runs in linear time in the input size. The second is a parallel algorithm that runs in O(n2/p) time on a linear array of p processors, p, 1 ≤ p ≤ n.

scholarly journals Implementation of Morphological Operation through Intercept Based Euclidean Distance Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2472-2475
Keyword(s):  
Euclidean Distance ◽  
Input Image ◽  
Morphological Operation ◽  
Binary Images ◽  
Fracture Length ◽  
Wide Range ◽  
Edge Points ◽  
Scanned Image ◽  
Index Value ◽  
Computed Tomography Image

Skeletonization is the process of extracting the region-based shape features that represents a common form of original object. It can be employed to binary images thatproduces alternative binary image as the resulting output. The main goal of thinning is to degrade particular foreground pixels from original input image. A pruning focused on removing short spurs present in an image. By Employing morphological operation for the process of extracting skeleton can be used in wide range of application. In this paper, we proposed a model which is a robust intercept based Euclidean distance (IBED) algorithm to perform morphological operation to extract the surface of the skeleton from the computed Tomography image. In this edge points of the skeleton have been analyzed over set extraction, hence the characterization has been improved by presenting a novel skeleton with edge points. The index value, edge points, Euclidean distance and fracture length have been calculated. The proposed technique is employed to four men and two women with different fractures in CT scanned image.

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 A Novel Method for Reconstructing Broken Contour Lines Extracted from Scanned Topographic Maps

2018 ◽  
Vol 1 ◽  
pp. 1-7 ◽  
Author(s):  
Feng Wang ◽  
Pingzhi Liu ◽  
Yun Yang ◽  
Haiping Wei ◽  
Xiaoya An
Keyword(s):  
Euclidean Distance ◽  
Weighted Average ◽  
Topographic Maps ◽  
Morphological Operations ◽  
End Points ◽  
Binary Images ◽  
Contour Lines ◽  
Easy Problem ◽  
Novel Method ◽  
Experimental Comparisons

It is known that after segmentation and morphological operations on scanned topographic maps, gaps occur in contour lines. It is also well known that filling these gaps and reconstruction of contour lines with high accuracy and completeness is not an easy problem. In this paper, a novel method is proposed dedicated in automatic or semiautomatic filling up caps and reconstructing broken contour lines in binary images. The key part of end points’ auto-matching and reconnecting is deeply discussed after introducing the procedure of reconstruction, in which some key algorithms and mechanisms are presented and realized, including multiple incremental backing trace to get weighted average direction angle of end points, the max constraint angle control mechanism based on the multiple gradient ranks, combination of weighted Euclidean distance and deviation angle to determine the optimum matching end point, bidirectional parabola control, etc. Lastly, experimental comparisons based on typically samples are complemented between proposed method and the other representative method, the results indicate that the former holds higher accuracy and completeness, better stability and applicability.

Sign in / Sign up

Export Citation Format

Share Document