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.
| Original language | English |
|---|---|
| Pages (from-to) | 1744-1754 |
| Number of pages | 11 |
| Journal | Pattern Recognition Letters |
| Volume | 27 |
| Issue number | 14 |
| DOIs | |
| Publication status | Published - 2006 Oct 15 |
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Keywords
- Edge image
- Line descriptor
- Principal component analysis (PCA)
- Straight line detection
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Electrical and Electronic Engineering
Cite this
A straight line detection using principal component analysis. / Lee, Yun Seok; Koo, Han S.; Jeong, Chang-Sung.
In: Pattern Recognition Letters, Vol. 27, No. 14, 15.10.2006, p. 1744-1754.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A straight line detection using principal component analysis
AU - Lee, Yun Seok
AU - Koo, Han S.
AU - Jeong, Chang-Sung
PY - 2006/10/15
Y1 - 2006/10/15
N2 - 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.
AB - 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.
KW - Edge image
KW - Line descriptor
KW - Principal component analysis (PCA)
KW - Straight line detection
UR - http://www.scopus.com/inward/record.url?scp=33747894575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33747894575&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2006.04.016
DO - 10.1016/j.patrec.2006.04.016
M3 - Article
AN - SCOPUS:33747894575
VL - 27
SP - 1744
EP - 1754
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
SN - 0167-8655
IS - 14
ER -