A straight line detection algorithm is presented. The algorithm separates
row and column edges from edge image using their primitive shapes.
The edges are labeled, and the principal component analysis (PCA)
is performed for each labeled edges. With the principal components,
the algorithm detects straight lines and their orientations, which
is useful for various intensive applications. Our algorithm overcomes
the disadvantages of Hough transform (HT) and other algorithms, i.e.
unknown grouping of collinear lines, complexity and local ambiguities.
The experimental results show the efficiency of our algorithm.
%0 Journal Article
%1 Lee2006
%A Lee, Yun-Seok
%A Koo, Han-Suh
%A Jeong, Chang-Sung
%D 2006
%K (PCA); Edge Line Principal Straight analysis component descriptor; detection; image line
%N 14
%P 1744-1754
%R dx.doi.org/10.1016/j.patrec.2006.04.016
%T A straight line detection using principal component analysis
%U http://www.sciencedirect.com/science/article/B6V15-4K7NHD5-2/2/42e61183f470c6fafcf64f4ec8033e79
%V 27
%X A straight line detection algorithm is presented. The algorithm separates
row and column edges from edge image using their primitive shapes.
The edges are labeled, and the principal component analysis (PCA)
is performed for each labeled edges. With the principal components,
the algorithm detects straight lines and their orientations, which
is useful for various intensive applications. Our algorithm overcomes
the disadvantages of Hough transform (HT) and other algorithms, i.e.
unknown grouping of collinear lines, complexity and local ambiguities.
The experimental results show the efficiency of our algorithm.
@article{Lee2006,
abstract = {A straight line detection algorithm is presented. The algorithm separates
row and column edges from edge image using their primitive shapes.
The edges are labeled, and the principal component analysis (PCA)
is performed for each labeled edges. With the principal components,
the algorithm detects straight lines and their orientations, which
is useful for various intensive applications. Our algorithm overcomes
the disadvantages of Hough transform (HT) and other algorithms, i.e.
unknown grouping of collinear lines, complexity and local ambiguities.
The experimental results show the efficiency of our algorithm.},
added-at = {2011-03-27T19:35:34.000+0200},
author = {Lee, Yun-Seok and Koo, Han-Suh and Jeong, Chang-Sung},
biburl = {https://www.bibsonomy.org/bibtex/22682cd3fcdaf14a46dc1b03ddc419773/cocus},
doi = {dx.doi.org/10.1016/j.patrec.2006.04.016},
file = {:./Lee.pdf:PDF},
interhash = {51a9736aeb0757293155ed8e8cffb56e},
intrahash = {2682cd3fcdaf14a46dc1b03ddc419773},
issn = {0162-8828},
journaltitle = {#patrec#},
keywords = {(PCA); Edge Line Principal Straight analysis component descriptor; detection; image line},
number = 14,
owner = {CK},
pages = {1744-1754},
review = {nur für TU Angestellte downloadbar},
timestamp = {2011-03-27T19:35:41.000+0200},
title = {A straight line detection using principal component analysis},
url = {http://www.sciencedirect.com/science/article/B6V15-4K7NHD5-2/2/42e61183f470c6fafcf64f4ec8033e79},
volume = 27,
year = 2006
}