In this paper, we propose a new method for extracting line segments
from edge images. Our method basically follows a line segment grouping
approach. This approach has many advantages over a Hough transform
based approach in practical situations. However, since the process
of the conventional line segment grouping approach is purely local,
it does not provide a mechanism for finding more favorable line segments
from a global point of view. Our method overcomes the local nature
of the conventional line segment grouping approach, while retaining
most of its advantages, by incorporating the useful concept of the
Hough transform based approach into the line segment grouping approach.
Our method is fast and allows elementary line segments to be shared
simultaneously by several line segments, and the degree of sharing
is determined by a user-specified threshold. We performed a series
of tests to compare the performance of our method with that of six
other methods. Throughout the tests our method ranked in the top
two of the tested methods both in detection rate and computation
time.
%0 Journal Article
%1 Jang2002
%A Jang, Jeong-Hun
%A Hong, Ki-Sang
%D 2002
%K Hough Line detection; grouping; segment transform
%N 10
%P 2235--2247
%R 10.1016/S0031-3203(01)00175-3
%T Fast line segment grouping method for finding globally more favorable
line segments
%U http://www.sciencedirect.com/science/article/B6V14-463FTWW-5/2/741fde17ba832e50d2eaf1404994ad9a
%V 35
%X In this paper, we propose a new method for extracting line segments
from edge images. Our method basically follows a line segment grouping
approach. This approach has many advantages over a Hough transform
based approach in practical situations. However, since the process
of the conventional line segment grouping approach is purely local,
it does not provide a mechanism for finding more favorable line segments
from a global point of view. Our method overcomes the local nature
of the conventional line segment grouping approach, while retaining
most of its advantages, by incorporating the useful concept of the
Hough transform based approach into the line segment grouping approach.
Our method is fast and allows elementary line segments to be shared
simultaneously by several line segments, and the degree of sharing
is determined by a user-specified threshold. We performed a series
of tests to compare the performance of our method with that of six
other methods. Throughout the tests our method ranked in the top
two of the tested methods both in detection rate and computation
time.
@article{Jang2002,
abstract = {In this paper, we propose a new method for extracting line segments
from edge images. Our method basically follows a line segment grouping
approach. This approach has many advantages over a Hough transform
based approach in practical situations. However, since the process
of the conventional line segment grouping approach is purely local,
it does not provide a mechanism for finding more favorable line segments
from a global point of view. Our method overcomes the local nature
of the conventional line segment grouping approach, while retaining
most of its advantages, by incorporating the useful concept of the
Hough transform based approach into the line segment grouping approach.
Our method is fast and allows elementary line segments to be shared
simultaneously by several line segments, and the degree of sharing
is determined by a user-specified threshold. We performed a series
of tests to compare the performance of our method with that of six
other methods. Throughout the tests our method ranked in the top
two of the tested methods both in detection rate and computation
time.},
added-at = {2011-03-27T19:35:34.000+0200},
author = {Jang, Jeong-Hun and Hong, Ki-Sang},
biburl = {https://www.bibsonomy.org/bibtex/2418e932214504aa725636b9f9f4073ac/cocus},
doi = {10.1016/S0031-3203(01)00175-3},
file = {:./Jang2002.pdf:PDF},
interhash = {8c2eaf67841f774939929eba899433a6},
intrahash = {418e932214504aa725636b9f9f4073ac},
journaltitle = {#PR#},
keywords = {Hough Line detection; grouping; segment transform},
number = 10,
owner = {CK},
pages = {2235--2247},
timestamp = {2011-03-27T19:35:40.000+0200},
title = {Fast line segment grouping method for finding globally more favorable
line segments},
url = {http://www.sciencedirect.com/science/article/B6V14-463FTWW-5/2/741fde17ba832e50d2eaf1404994ad9a},
volume = 35,
year = 2002
}