A straight line detection using principal component analysis

Yun Seok Lee; Han Suh Koo; Chang Sung Jeong

doi:10.1016/j.patrec.2006.04.016

A straight line detection using principal component analysis

Yun Seok Lee, Han Suh Koo, Chang Sung Jeong

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

79 Citations (Scopus)

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	https://doi.org/10.1016/j.patrec.2006.04.016
Publication status	Published - 2006 Oct 15

Keywords

Edge image
Line descriptor
Principal component analysis (PCA)
Straight line detection

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patrec.2006.04.016

Cite this

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title = "A straight line detection using principal component analysis",

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.",

keywords = "Edge image, Line descriptor, Principal component analysis (PCA), Straight line detection",

author = "Lee, {Yun Seok} and Koo, {Han Suh} and Jeong, {Chang Sung}",

note = "Funding Information: This work was supported by the Brain Korea 21 and KIPA Information Technology Research Center. The Pentagon image was obtained from Carnegie Mellon Image Database and all the rest of the source images in experiment section were obtained from Visual Geometry Group at Oxford University. ",

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T1 - A straight line detection using principal component analysis

AU - Lee, Yun Seok

AU - Koo, Han Suh

AU - Jeong, Chang Sung

N1 - Funding Information: This work was supported by the Brain Korea 21 and KIPA Information Technology Research Center. The Pentagon image was obtained from Carnegie Mellon Image Database and all the rest of the source images in experiment section were obtained from Visual Geometry Group at Oxford University.

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

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

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ER -

A straight line detection using principal component analysis

Abstract

Keywords

ASJC Scopus subject areas

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